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Big Data Analytics Lectures |  Collaborative  Filtering with Solved Example in Hindi
 
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visit our website for full course www.lastmomenttuitions.com here we have discussed the filtering technique which is collaborative filtering Simply "Here we need to make a recommendation system which will recommend user a particular product based on the choices of other user's." NOTES: https://lastmomenttuitions.com/how-to-buy-notes/ bda notes form : https://goo.gl/Ti9CQj introduction to Hadoop : https://goo.gl/LCHC7Q Introduction to Hadoop part 2 : https://goo.gl/jSSxu2 Distance Measures : https://goo.gl/1NL3qF Euclidean Distance : https://goo.gl/6C16RJ Jaccard distance : https://goo.gl/C6vmWR Cosine Distance : https://goo.gl/Sm48Ny Edit Distance : https://goo.gl/dG3jAP Hamming Distance : https://goo.gl/KNw95L FM Flajolit martin Algorithm : https://goo.gl/ybjX9V Random Sampling Algorithm : https://goo.gl/YW1AWh PCY ( park chen yu) algorithm : https://goo.gl/HVWs21 Collaborative Filtering : https://goo.gl/GBQ7JW Bloom Filter Basic concept : https://goo.gl/uHjX5B Naive Bayes Classifier : https://goo.gl/dbRYYh Naive Bayes Classifier part2 : https://goo.gl/LWstNv Decision Tree : https://goo.gl/5m8JhA Apriori Algorithm :https://goo.gl/mmpxL6 FP TREE Algorithm : https://goo.gl/S29yV8 Agglomerative clustering algorithmn : https://goo.gl/L9nGu8 Hubs and Authority and Hits Algorithm : https://goo.gl/D2EdFG Betweenness Centrality : https://goo.gl/czZZJR
Views: 6794 Last moment tuitions
Collaborative Filtering Based Recommendation of Online Social Voting
 
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Collaborative Filtering Based Recommendation of Online Social Voting Java Project. Download Collaborative Filtering Based Recommendation of Online Social Voting Project Code, Report and PPT Contact :+91 7702177291, +91 9052016340 Email : [email protected] Website : www.1000projects.org
Views: 395 1000 Projects
Serendipitous Recommendation in E Commerce Using Innovator Based Collaborative Filtering
 
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Serendipitous Recommendation in E-Commerce Using Innovator-Based Collaborative Filtering Latest IEEE Projects 2018-2019 IEEE Dataminig java Projects Topics Free download Thanks & Regards, 1Crore Projects java data mining IEEE Projects , Head Office: No.68 & 70 ,Ground Floor, Raahat Plaza, Vadapalani,Chennai - 26. Contact: +91 7708150152 , +91 9751800789 Web :- http://1croreprojects.com/ E-Mail ID : [email protected]
Views: 129 1 Crore Projects
What is Data Mining and its applications
 
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Views: 99 mayur humbal
Lecture 41 — Overview of Recommender Systems | Stanford University
 
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. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .
Collaborative Filtering-Based Recommendation of Online Social Voting
 
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Collaborative Filtering-Based Recommendation of Online Social Voting To get this project in ONLINE or through TRAINING Sessions, Contact: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83.Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #37, Kamaraj Salai,Thattanchavady, Puducherry -9.Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690, Email: [email protected], web: http://www.jpinfotech.org Social voting is an emerging new feature in online social networks. It poses unique challenges and opportunities for recommendation. In this paper, we develop a set of matrix factorization (MF) and nearest-neighbor (NN)-based recommender systems (RSs) that explore user social network and group affiliation information for social voting recommendation. Through experiments with real social voting traces, we demonstrate that social network and group affiliation information can significantly improve the accuracy of popularity-based voting recommendation, and social network information dominates group affiliation information in NN-based approaches. We also observe that social and group information is much more valuable to cold users than to heavy users. In our experiments, simple metapath based NN models outperform computation-intensive MF models in hot-voting recommendation, while users’ interests for non-hot votings can be better mined by MF models. We further propose a hybrid RS, bagging different single approaches to achieve the best top-k hit rate.
Views: 755 jpinfotechprojects
Typicality-Based Collaborative Filtering Recommendation
 
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To get this project in ONLINE or through TRAINING Sessions, Contact:JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83. Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai, Thattanchavady, Puducherry -9. Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690 , Email: [email protected], web: www.jpinfotech.org Blog: www.jpinfotech.blogspot.com Typicality-Based Collaborative Filtering Recommendation Collaborative filtering (CF) is an important and popular technology for recommender systems. However, current CF methods suffer from such problems as data sparsity, recommendation inaccuracy, and big-error in predictions. In this paper, we borrow ideas of object typicality from cognitive psychology and propose a novel typicality-based collaborative filtering recommendation method named TyCo. A distinct feature of typicality-based CF is that it finds “neighbors” of users based on user typicality degrees in user groups (instead of the corated items of users, or common users of items, as in traditional CF). To the best of our knowledge, there has been no prior work on investigating CF recommendation by combining object typicality. TyCo outperforms many CF recommendation methods on recommendation accuracy (in terms of MAE) with an improvement of at least 6.35 percent in Movielens data set, especially with sparse training data (9.89 percent improvement on MAE) and has lower time cost than other CF methods. Further, it can obtain more accurate predictions with less number of big-error predictions.
Views: 1394 jpinfotechprojects
R Tutorial For Beginners | R Programming Tutorial l R Language For Beginners | R Training | Edureka
 
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( R Training : https://www.edureka.co/r-for-analytics ) This Edureka R Tutorial (R Tutorial Blog: https://goo.gl/mia382) will help you in understanding the fundamentals of R tool and help you build a strong foundation in R. Below are the topics covered in this tutorial: 1. Why do we need Analytics ? 2. What is Business Analytics ? 3. Why R ? 4. Variables in R 5. Data Operator 6. Data Types 7. Flow Control 8. Plotting a graph in R Check out our R Playlist: https://goo.gl/huUh7Y Subscribe to our channel to get video updates. Hit the subscribe button above. #R #Rtutorial #Ronlinetraining #Rforbeginners #Rprogramming How it Works? 1. This is a 5 Week Instructor led Online Course, 30 hours of assignment and 20 hours of project work 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will be working on a real time project for which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - - - - About the Course edureka's Data Analytics with R training course is specially designed to provide the requisite knowledge and skills to become a successful analytics professional. It covers concepts of Data Manipulation, Exploratory Data Analysis, etc before moving over to advanced topics like the Ensemble of Decision trees, Collaborative filtering, etc. During our Data Analytics with R Certification training, our instructors will help you: 1. Understand concepts around Business Intelligence and Business Analytics 2. Explore Recommendation Systems with functions like Association Rule Mining , user-based collaborative filtering and Item-based collaborative filtering among others 3. Apply various supervised machine learning techniques 4. Perform Analysis of Variance (ANOVA) 5. Learn where to use algorithms - Decision Trees, Logistic Regression, Support Vector Machines, Ensemble Techniques etc 6. Use various packages in R to create fancy plots 7. Work on a real-life project, implementing supervised and unsupervised machine learning techniques to derive business insights - - - - - - - - - - - - - - - - - - - Who should go for this course? This course is meant for all those students and professionals who are interested in working in analytics industry and are keen to enhance their technical skills with exposure to cutting-edge practices. This is a great course for all those who are ambitious to become 'Data Analysts' in near future. This is a must learn course for professionals from Mathematics, Statistics or Economics background and interested in learning Business Analytics. - - - - - - - - - - - - - - - - Why learn Data Analytics with R? The Data Analytics with R training certifies you in mastering the most popular Analytics tool. "R" wins on Statistical Capability, Graphical capability, Cost, rich set of packages and is the most preferred tool for Data Scientists. Below is a blog that will help you understand the significance of R and Data Science: Mastering R Is The First Step For A Top-Class Data Science Career Having Data Science skills is a highly preferred learning path after the Data Analytics with R training. Check out the upgraded Data Science Course For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free). Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 441932 edureka!
Lecture 55 — Latent Factor Recommender System  | Stanford University
 
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. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .
User Based Collaborative Filtering in Rapidminer
 
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This is an educational video related to user based collaborative filtering in Rapidminer click here to download dataset: https://bit.ly/2DwSO8w
Views: 2348 Kwik Deals
Mining Competitors from Large Unstructured Datasets
 
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Mining Competitors from Large Unstructured Datasets To get this project in ONLINE or through TRAINING Sessions, Contact: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83.Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #37, Kamaraj Salai,Thattanchavady, Puducherry -9.Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690, Email: [email protected], web: http://www.jpinfotech.org In any competitive business, success is based on the ability to make an item more appealing to customers than the competition. A number of questions arise in the context of this task: how do we formalize and quantify the competitiveness between two items? Who are the main competitors of a given item? What are the features of an item that most affect its competitiveness? Despite the impact and relevance of this problem to many domains, only a limited amount of work has been devoted toward an effective solution. In this paper, we present a formal definition of the competitiveness between two items, based on the market segments that they can both cover. Our evaluation of competitiveness utilizes customer reviews, an abundant source of information that is available in a wide range of domains. We present efficient methods for evaluating competitiveness in large review datasets and address the natural problem of finding the top-k competitors of a given item. Finally, we evaluate the quality of our results and the scalability of our approach using multiple datasets from different domains.
Views: 1510 jpinfotechprojects
Collaborative Filtering vs Content Based Filtering in 1 min.
 
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Recommender Systems,Advanced Databases and Information Retrieval,Pune University. This is my understanding of it.CAN BE WRONG.Not an expert.
Views: 1093 Darshan Shah
RecSys 2016: Paper Session 6 - Deep Neural Networks for YouTube Recommendations
 
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Yuri M. Brovman, Marie Jacob, Natraj Srinivasan, Stephen Neola, Daniel Galron, Ryan Snyder, Paul Wang https://doi.org/10.1145/2959100.2959166 This paper tackles the problem of recommendations in eBay's large semi-structured marketplace. eBay's variable inventory and lack of structured information about listings makes traditional collaborative filtering algorithms difficult to use. We discuss how to overcome these data limitations to produce high quality recommendations in real time with a combination of a customized scalable architecture as well as a widely applicable machine learned ranking model. A pointwise ranking approach is utilized to reduce the ranking problem to a binary classification problem optimized on past user purchase behavior. We present details of a sampling strategy and feature engineering that have been critical to achieve a lift in both purchase through rate (PTR) and revenue.
Views: 5506 ACM RecSys
Machine Learning :  Introduction (in Hindi)
 
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Machine Learning Machine learning is a subfield of computer science (CS) and artificial intelligence (AI) that deals with the construction and study of systems that can learn from data, rather than follow only explicitly programmed instructions. Besides CS and AI, it has strong ties to statistics and optimization, which deliver both methods and theory to the field. Machine learning is employed in a range of computing tasks where designing and programming explicit, rule-based algorithms is infeasible. Example applications include spam filtering, optical character recognition (OCR), search engines and computer vision. Machine learning, data mining, and pattern recognition are sometimes conflated. Machine learning tasks can be of several forms. In supervised learning, the computer is presented with example inputs and their desired outputs, given by a “teacher”, and the goal is to learn a general rule that maps inputs to outputs. Spam filtering is an example of supervised learning. In unsupervised learning, no labels are given to the learning algorithm, leaving it on its own to groups of similar inputs (clustering), density estimates orprojections of high-dimensional data that can be visualised effectively. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end. Topic modeling is an example of unsupervised learning, where a program is given a list of human language documents and is tasked to find out which documents cover similar topics. In reinforcement learning, a computer program interacts with a dynamic environment in which it must perform a certain goal (such as driving a vehicle), without a teacher explicitly telling it whether it has come close to its goal or not. Definition In 1959, Arthur Samuel defined machine learning as a “Field of study that gives computers the ability to learn without being explicitly programmed”. Tom M. Mitchell provided a widely quoted, more formal definition: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E”. This definition is notable for its defining machine learning in fundamentally operational rather than cognitive terms, thus following Alan Turing's proposal in Turing's paper “Computing Machinery and Intelligence” that the question “Can machines think?” be replaced with the question “Can machines do what we (as thinking entities) can do?” Generalization: A core objective of a learner is to generalize from its experience. Generalization in this context is the ability of a learning machine to perform accurately on new, unseen tasks after having experienced a learning data set. The training examples come from some generally unknown probability distribution (considered representative of the space of occurrences) and the learner has to build a general model about this space that enables it to produce sufficiently accurate predictions in new cases. These two terms are commonly confused, as they often employ the same methods and overlap significantly. They can be roughly defined as follows: 1. Machine learning focuses on prediction, based on known properties learned from the training data. 2. Data Mining focuses on the discovery of (previously)unknown properties in the data. This is the analysis step of Knowledge Discovery in Databases. The two areas overlap in many ways: data mining uses many machine learning methods, but often with a slightly different goal in mind. On the other hand, machine learning also employs data mining methods as “unsupervised learning” or as a preprocessing step to improve learner accuracy. Human Interaction Some machine learning systems attempt to eliminate the need for human intuition in data analysis, while others adopt a collaborative approach between human and machine
Views: 23341 sangram singh
Personalized Music Recommendation by Mining Social Media Tags
 
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Title: Personalized Music Recommendation by Mining Social Media Tags Domain: Data Mining Description: Over the past few years, the recommender system has been proposed as a critical role to help users choose the preferred product from a massive amount of data. For music recommendation, most recent recommender systems made attempts to associate music with the user’s preferences primarily based on user ratings. However, this kind of recommendation mechanism encounters the problem called rating diversity that makes the prediction results unreliable. To cope with this problem, in this paper, we propose a novel music recommendation approach that utilizes social media tags instead of ratings to calculate the similarity between music pieces. Through the proposed tag-based similarity, the user preferences hidden in tags can be inferred effectively. The empirical evaluations on real social media datasets reveal that our proposed approach using social tags outperforms the existing ones using only ratings in terms of predicting the user’s preferences to music. For more details contact: E-Mail: [email protected] Buy Whole Project Kit for Rs 5000%. Project Kit: • 1 Review PPT • 2nd Review PPT • Full Coding with described algorithm • Video File • Full Document Note: *For bull purchase of projects and for outsourcing in various domains such as Java, .Net, .PHP, NS2, Matlab, Android, Embedded, Bio-Medical, Electrical, Robotic etc. contact us. *Contact for Real Time Projects, Web Development and Web Hosting services. *Comment and share on this video and win exciting developed projects for free of cost. Search Terms: 1. 2017 ieee projects 2. latest ieee projects in java 3. latest ieee projects in data mining 4. 2017 – 2018 data mining projects 5. 2017 – 2018 best project center in Chennai 6. best guided ieee project center in Chennai 7. 2017 – 2018 ieee titles 8. 2017 – 2018 base paper 9. 2017 – 2018 java projects in Chennai, Coimbatore, Bangalore, and Mysore 10. time table generation projects 11. instruction detection projects in data mining, network security 12. 2017 – 2018 data mining weka projects 13. 2017 – 2018 b.e projects 14. 2017 – 2018 m.e projects 15. 2017 – 2018 final year projects 16. affordable final year projects 17. latest final year projects 18. best project center in Chennai, Coimbatore, Bangalore, and Mysore 19. 2017 Best ieee project titles 20. best projects in java domain 21. free ieee project in Chennai, Coimbatore, Bangalore, and Mysore 22. 2017 – 2018 ieee base paper free download 23. 2017 – 2018 ieee titles free download 24. best ieee projects in affordable cost 25. ieee projects free download 26. 2017 data mining projects 27. 2017 ieee projects on data mining 28. 2017 final year data mining projects 29. 2017 data mining projects for b.e 30. 2017 data mining projects for m.e 31. 2017 latest data mining projects 32. latest data mining projects 33. latest data mining projects in java 34. data mining projects in weka tool 35. data mining in intrusion detection system 36. intrusion detection system using data mining 37. intrusion detection system using data mining ppt 38. intrusion detection system using data mining technique 39. data mining approaches for intrusion detection 40. data mining in ranking system using weka tool 41. data mining projects using weka 42. data mining in bioinformatics using weka 43. data mining using weka tool 44. data mining tool weka tutorial 45. data mining abstract 46. data mining base paper 47. data mining research papers 2017 - 2018 48. 2017 - 2018 data mining research papers 49. 2017 data mining research papers 50. data mining IEEE Projects 52. data mining and text mining ieee projects 53. 2017 text mining ieee projects 54. text mining ieee projects 55. ieee projects in web mining 56. 2017 web mining projects 57. 2017 web mining ieee projects 58. 2017 data mining projects with source code 59. 2017 data mining projects for final year students 60. 2017 data mining projects in java 61. 2017 data mining projects for students
ClubCF A Clustering based Collaborative Filtering Approach for Big Data Application
 
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We are ready to provide guidance to successfully complete your projects. IEEE 2014 Projects : http://www.squaresoft.co.in/
Collaborative Filtering Based Recommendation of Online Social Voting
 
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2017 IEEE Transaction on Knowledge and Data Engineering For More Details::Contact::K.Manjunath - 09535866270 http://www.tmksinfotech.com and http://www.bemtechprojects.com 2017 and 2018 IEEE [email protected] TMKS Infotech,Bangalore
Views: 1305 manju nath
Building Recommendation Systems in Azure - Collaborative Filtering
 
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http://www.arewa.video Building Recommendation Systems in Azure - Collaborative Filtering
Seminar Project: (Intelligent Recommender System)
 
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System that get the resources (PDF, DOC, PPT) for learning, from the search engines, and filter them to get the ten best result of them. Then compine the system with Moodle (LMS) to make it easy on st
Views: 521 Hadi Hosnia
Lecture 38 — Bloom Filters | Mining of Massive Datasets | Stanford University
 
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. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .
Bridging Collaborative Filtering and Semi-Supervised Learning
 
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Bridging Collaborative Filtering and Semi-Supervised Learning: A Neural Approach for POI recommendation Carl Yang (University of Illinois, Urbana Champaign) Lanxiao Bai (University of Illinois, Urbana Champaign) Chao Zhang (University of Illinois, Urbana Champaign) Quan Yuan (University of Illinois, Urbana Champaign) Jiawei Han (University of Illinois, Urbana Champaign) Recommender system is one of the most popular data mining topics that keep drawing extensive attention from both academia and industry. Among them, POI (point of interest) recommendation is extremely practical but challenging: it greatly benefits both users and businesses in real-world life, but it is hard due to data scarcity and various context. While a number of algorithms attempt to tackle the problem \wrt~specific data and problem settings, they often fail when the scenarios change. In this work, we propose to devise a general and principled SSL (semi-supervised learning) framework, to alleviate data scarcity via smoothing among neighboring users and POIs, and treat various context by regularizing user preference based on context graphs. To enable such a framework, we develop PACE (Preference And Context Embedding), a deep neural architecture that jointly learns the embeddings of users and POIs to predict both user preference over POIs and various context associated with users and POIs. We show that PACE successfully bridges CF (collaborative filtering) and SSL by generalizing the \textit{de facto} methods matrix factorization of CF and graph Laplacian regularization of SSL. Extensive experiments on two real location-based social network datasets demonstrate the effectiveness of PACE. More on http://www.kdd.org/kdd2017/
Views: 295 KDD2017 video
Two Stage Friend Recommendation Based on Network Alignment and Series-Expansion of Probabilistic
 
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Two-Stage Friend Recommendation Based on Network Alignment and Series-Expansion of Probabilistic Topic Model Java Project Source Code, Report and PPT. Concept: Friend recommendation is the most common feature in most of the social networking websites which helps users to find related friends . In existing system we have recommendation based on normal interest which is a single stage recommendation which dosent provide better results. In order to overcome this problem we design two stage friend recommendation system which considers different factors which is explained in the video. when ever user tags posts and shares on his profile he can get list of users who are of same interest from different social network platforms and display first stage of list. In second stage these friends are again filtered based on interest and type of message . In this way a effective way of friend recommendation can be designed. You can watch video for more details and functionality. Contact :+91 7702177291, +91 9052016340 Email : [email protected] Website : www.1000projects.org
Views: 251 1000 Projects
Lecture 58 — Overview of Clustering | Mining of Massive Datasets | Stanford University
 
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. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .
Personalized Music Recommendation by Mining Social Media Tags
 
01:11
Title: Personalized Music Recommendation by Mining Social Media Tags Domain: Data Mining Description: Over the past few years, the recommender system has been proposed as a critical role to help users choose the preferred product from a massive amount of data. For music recommendation, most recent recommender systems made attempts to associate music with the user’s preferences primarily based on user ratings. However, this kind of recommendation mechanism encounters the problem called rating diversity that makes the prediction results unreliable. To cope with this problem, in this paper, we propose a novel music recommendation approach that utilizes social media tags instead of ratings to calculate the similarity between music pieces. Through the proposed tag-based similarity, the user preferences hidden in tags can be inferred effectively. The empirical evaluations on real social media datasets reveal that our proposed approach using social tags outperforms the existing ones using only ratings in terms of predicting the user’s preferences to music. Buy Whole Project Kit for Rs 5000%. Project Kit: • 1 Review PPT • 2nd Review PPT • Full Coding with described algorithm • Video File • Full Document Note: *For bull purchase of projects and for outsourcing in various domains such as Java, .Net, .PHP, NS2, Matlab, Android, Embedded, Bio-Medical, Electrical, Robotic etc. contact us. *Contact for Real Time Projects, Web Development and Web Hosting services. *Comment and share on this video and win exciting developed projects for free of cost. contact us for more details: 044-43548566,8110081181 [email protected]
Collaborative Filtering Based Recommendation of Online Social Voting
 
13:42
2017 IEEE Transaction on Knowledge and Data Engineering For More Details::Contact::K.Manjunath - 09535866270 http://www.tmksinfotech.com and http://www.bemtechprojects.com 2017 and 2018 IEEE [email protected] TMKS Infotech,Bangalore
Views: 26 Manju nath
Movie Recommendation System with RapidMiner (in Turkish)
 
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The csv files and xml files of the processes can be downloaded from following link: https://github.com/inancarin/RapidMiner/tree/master/Recommendation%20System
Views: 1704 İnanç Arın
Collaborative Variational Autoencoder for Recommender Systems
 
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Collaborative Variational Autoencoder for Recommender Systems Xiaopeng Li (The Hong Kong University of Science and Technology) James She (The Hong Kong University of Science and Technology) Modern recommender systems usually employ collaborative filtering with rating information to recommend items to users due to its successful performance. However, because of the drawbacks of collaborative-based methods such as sparsity, cold start, etc., more attention has been drawn to hybrid methods that consider both the rating and content information. Most of the previous works in this area cannot learn a good representation from content for recommendation task or consider only text modality of the content, thus their methods are very limited in current multimedia scenario. This paper proposes a Bayesian generative model called collaborative variational autoencoder (CVAE) that considers both rating and content for recommendation in multimedia scenario. The model learns deep latent representations from content data in an unsupervised manner and also learns implicit relationships between items and users from both content and rating. Unlike previous works with denoising criteria, the proposed CVAE learns a latent distribution for content in latent space instead of observation space through an inference network and can be easily extended to other multimedia modalities other than text. Experiments show that CVAE is able to significantly outperform the state-of-the-art recommendation methods with more robust performance. More on http://www.kdd.org/kdd2017/
Views: 1055 KDD2017 video
Demo of multi-user content recommendations
 
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The European FP7 R&D project HBB-Next (http://hbb-next.eu) presents a proof-of-concept of multi-user content recommendations. A QR-code scanner is used to identify which persons are watching TV. Then some clever algorithms ("genre-based collaborative filtering", "least-misery preference combination") calculate a set of TV watching recommendations to the users that are present. This video was made by TNO. It includes contrivbutions by HBB-Next partners IRT, THM and KU Leuven. Disclaimer: This video is showing work-in-progress of a European research project. It is for technical demonstration purposes only and does not reflect any application guidelines from the content provider. It does not preview plans of future products by any broadcaster, content provider or other party.
Views: 194 HBB-Next
Typicality Based Collaborative Filtering Recommendation
 
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Typicality Based Collaborative Filtering Recommendation +91-9994232214,8144199666, [email protected], www.projectsieee.com, www.ieee-projects-chennai.com IEEE PROJECTS 2014 ----------------------------------- Contact:+91-9994232214,+91-8144199666 Email:[email protected] http://ieee.projectsieee.com/Cloud-Computing http://ieee.projectsieee.com/Data-Mining http://ieee.projectsieee.com/Android http://ieee.projectsieee.com/Image-Processing http://ieee.projectsieee.com/Networking http://ieee.projectsieee.com/Network-Security http://ieee.projectsieee.com/Mobile-Computing http://ieee.projectsieee.com/Parallel-Distributed http://ieee.projectsieee.com/Wireless-Communication http://ieee.projectsieee.com/NS2-Projects http://ieee.projectsieee.com/Matlab Support: ------------- Projects Code Documentation PPT Projects Video File Projects Explanation Teamviewer Support
Views: 81 PROJECTS2014
Clustering Using Representatives [CURE]
 
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Big Data Analytics For more http://www.anuradhabhatia.com
Views: 6547 Anuradha Bhatia
ggplot2 Tutorial | ggplot2 In R Tutorial | Data Visualization In R | R Training | Edureka
 
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( R Training : https://www.edureka.co/r-for-analytics ) This "ggplot2 Tutorial" by Edureka is a comprehensive session on the ggplot2 in R. This tutorial will not only get you started with the ggplot2 package, but also make you an expert in visualizing data with the help of this package. This tutorial will comprise of these topics: 1) Base R Graphics 2) Grammar of Graphics 3) GGPLOT2 package Check out our R Playlist: https://goo.gl/huUh7Y Subscribe to our channel to get video updates. Hit the subscribe button above. #R #Rtutorial #Ronlinetraining #ggplot2 #ggplotinr How it Works? 1. This is a 5 Week Instructor led Online Course, 30 hours of assignment and 20 hours of project work 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will be working on a real time project for which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - - - - About the Course edureka's Data Analytics with R training course is specially designed to provide the requisite knowledge and skills to become a successful analytics professional. It covers concepts of Data Manipulation, Exploratory Data Analysis, etc before moving over to advanced topics like the Ensemble of Decision trees, Collaborative filtering, etc. During our Data Analytics with R Certification training, our instructors will help you: 1. Understand concepts around Business Intelligence and Business Analytics 2. Explore Recommendation Systems with functions like Association Rule Mining , user-based collaborative filtering and Item-based collaborative filtering among others 3. Apply various supervised machine learning techniques 4. Perform Analysis of Variance (ANOVA) 5. Learn where to use algorithms - Decision Trees, Logistic Regression, Support Vector Machines, Ensemble Techniques etc 6. Use various packages in R to create fancy plots 7. Work on a real-life project, implementing supervised and unsupervised machine learning techniques to derive business insights - - - - - - - - - - - - - - - - - - - Who should go for this course? This course is meant for all those students and professionals who are interested in working in analytics industry and are keen to enhance their technical skills with exposure to cutting-edge practices. This is a great course for all those who are ambitious to become 'Data Analysts' in near future. This is a must learn course for professionals from Mathematics, Statistics or Economics background and interested in learning Business Analytics. - - - - - - - - - - - - - - - - Why learn Data Analytics with R? The Data Analytics with R training certifies you in mastering the most popular Analytics tool. "R" wins on Statistical Capability, Graphical capability, Cost, rich set of packages and is the most preferred tool for Data Scientists. Below is a blog that will help you understand the significance of R and Data Science: Mastering R Is The First Step For A Top-Class Data Science Career Having Data Science skills is a highly preferred learning path after the Data Analytics with R training. Check out the upgraded Data Science Course For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free). Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 32391 edureka!
2014 IEEE DATA MINING A Cocktail Approach for Travel Package Recommendation
 
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To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - [email protected] Our Website: www.globalsofttechnologies.org
Visual Cryptography and Image Processing Based Approach
 
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Visual Cryptography and Image Processing Based Approach for Secure Transactions in Banking Sector -IEEE PROJECTS 2018-2019 TO GET THE PROJECT CODE...CONTACT www.matlabprojectscode.com https://www.facebook.com/Matlab-source-code-1062809320466572/ e-mail : [email protected] Call: +91 8300015425
User Centric Similarity Search
 
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User Centric Similarity Search Java Project. Download User Centric Similarity Search Java Project Code, Report and PPT Contact :+91 7702177291, +91 9052016340 Email : [email protected] Website : www.1000projects.org
Views: 321 1000 Projects
A System to Filter Unwanted Messages from OSN User Walls
 
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Title:A System to Filter Unwanted Messages from OSN User Walls Domain: Data Mining Abstract: One fundamental issue in today's Online Social Networks (OSNs) is to give users the ability to control the messages posted on their own private space to avoid that unwanted content is displayed. Up to now, OSNs provide little support to this requirement. To fill the gap, in this paper, we propose a system allowing OSN users to have a direct control on the messages posted on their walls. This is achieved through a flexible rule-based system, that allows users to customize the filtering criteria to be applied to their walls, and a Machine Learning-based soft classifier automatically labeling messages in support of content-based filtering. Concepts: What is Content-Based Filtering? Information filtering systems are designed to classify a stream of dynamically generated information dispatched asynchronously by an information producer and present to the user those information that are likely to satisfy his/her requirements. In content-based filtering, each user is assumed to operate independently. As a result, a content-based filtering system selects information items based on the correlation between the content of the items and the user preferences as opposed to a collaborative filtering system that chooses items based on the correlation between people with similar preferences. What is FILTERED WALL ARCHITECTURE? The architecture in support of OSN services is a three-tier structure. The first layer, called Social Network Manager (SNM), commonly aims to provide the basic OSN functionalities (i.e., profile and relationship management), whereas the second layer provides the support for external Social Network Applications (SNAs).4 The supported SNAs may in turn require an additional layer for their needed Graphical User Interfaces (GUIs). According to this reference architecture, the proposed system is placed in the second and third layers. What is Text Representation? The extraction of an appropriate set of features by which representing the text of a given document is a crucial task strongly affecting the performance of the overall classification strategy. Different sets of features for text categorization have been proposed in the literature; however, the most appropriate feature set and feature representation for short text messages have not yet been sufficiently investigated. For more details contact: E-Mail: [email protected] Purchase The Whole Project Kit Project Kit: • 1 Review PPT • 2nd Review PPT • Full Coding with described algorithm • Video File • Full Document Note: *For bull purchase of projects and for outsourcing in various domains such as Java, .Net, .PHP, NS2, Matlab, Android, Embedded, Bio-Medical, Electrical, Robotic etc. contact us. *Contact for Real Time Projects, Web Development and Web Hosting services. *Comment and share on this video and win exciting developed projects for free of cost. Search Terms: 1. 2017 ieee projects 2. latest ieee projects in java 3. latest ieee projects in data mining 4. 2017 – 2018 data mining projects 5. 2017 – 2018 best project center in Chennai 6. best guided ieee project center in Chennai 7. 2017 – 2018 ieee titles 8. 2017 – 2018 base paper 9. 2017 – 2018 java projects in Chennai, Coimbatore, Bangalore, and Mysore 10. time table generation projects 11. instruction detection projects in data mining, network security 12. 2017 – 2018 data mining weka projects 13. 2017 – 2018 b.e projects 14. 2017 – 2018 m.e projects 15. 2017 – 2018 final year projects 16. affordable final year projects 17. latest final year projects 18. best project center in Chennai, Coimbatore, Bangalore, and Mysore 19. 2017 Best ieee project titles 20. best projects in java domain 21. free ieee project in Chennai, Coimbatore, Bangalore, and Mysore 22. 2017 – 2018 ieee base paper free download 23. 2017 – 2018 ieee titles free download 24. best ieee projects in affordable cost 25. ieee projects free download 26. 2017 data mining projects 27. 2017 ieee projects on data mining 28. 2017 final year data mining projects 29. 2017 data mining projects for b.e 30. 2017 data mining projects for m.e 31. 2017 latest data mining projects 32. latest data mining projects 33. latest data mining projects in java 34. data mining projects in weka tool 35. data mining in intrusion detection system 36. intrusion detection system using data mining 37. intrusion detection system using data mining ppt 38. intrusion detection system using data mining technique 39. data mining approaches for intrusion detection 40. data mining in ranking system using weka tool 41. data mining projects using weka 42. data mining in bioinformatics using weka 43. data mining using weka tool 44. data mining tool weka tutorial 45. data mining abstract 46. data mining base paper 47. data mining research papers 2017 - 2018 48. 2017 - 2018 data mining research papers 49. 2017 data mining research papers
Views: 1747 InnovationAdsOfIndia
A recommendation system based on hierarchical clustering of an article level citation network
 
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A recommendation system based on hierarchical clustering of an article level citation network 2016-2017 MICANS INFOTECH offers Projects in CSE ,IT, EEE, ECE, MECH , MCA. MPHIL , BSC, in various domains JAVA ,PHP, DOT NET , ANDROID , MATLAB , NS2 , EMBEDDED , VLSI , APPLICATION PROJECTS , IEEE PROJECTS. CALL : +91 90036 28940 +91 94435 11725 [email protected] WWW.MICANSINFOTECH.COM COMPANY PROJECTS, INTERNSHIP TRAINING, MECHANICAL PROJECTS, ANSYS PROJECTS, CAD PROJECTS, CAE PROJECTS, DESIGN PROJECTS, CIVIL PROJECTS, IEEE MCA PROJECTS, IEEE M.TECH PROJECTS, IEEE PROJECTS, IEEE PROJECTS IN PONDY, IEEE PROJECTS, EMBEDDED PROJECTS, ECE PROJECTS PONDICHERRY, DIPLOMA PROJECTS, FABRICATION PROJECTS, IEEE PROJECTS CSE, IEEE PROJECTS CHENNAI, IEEE PROJECTS CUDDALORE, IEEE PROJECTS IN PONDICHERRY, PROJECT DEVELOPMENT CENTRE
SociRank Identifying and Ranking Prevalent News Topics Using Social Media Factors
 
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2017-2018 Java IEEE Project SociRank Identifying and Ranking Prevalent News Topics Using Social Media Factors Abstract: Mass media sources, specifically the news media, have traditionally informed us of daily events. In modern times, social media services such as Twitter provide an enormous amount of user-generated data, which have great potential to contain informative news-related content. For these resources to be useful, we must find a way to filter noise and only capture the content that, based on its similarity to the news media, is considered valuable. However, even after noise is removed, information overload may still exist in the remaining data-hence, it is convenient to prioritize it for consumption. To achieve prioritization, information must be ranked in order of estimated importance considering three factors. First, the temporal prevalence of a particular topic in the news media is a factor of importance, and can be considered the media focus (MF) of a topic. Second, the temporal prevalence of the topic in social media indicates its user attention (UA). Last, the interaction between the social media users who mention this topic indicates the strength of the community discussing it, and can be regarded as the user interaction (UI) toward the topic. We propose an unsupervised framework-SociRank-which identifies news topics prevalent in both social media and the news media, and then ranks them by relevance using their degrees of MF, UA, and UI. Our experiments show that SociRank improves the quality and variety of automatically identified news topics.
Views: 906 1 Crore Projects
R Programming For Beginners | R Language Tutorial | R Tutorial For Beginners | Edureka
 
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( R Training : https://www.edureka.co/r-for-analytics ) This Edureka R Programming Tutorial For Beginners (R Tutorial Blog: https://goo.gl/mia382) will help you in understanding the fundamentals of R and will help you build a strong foundation in R. Below are the topics covered in this tutorial: 1. Variables 2. Data types 3. Operators 4. Conditional Statements 5. Loops 6. Strings 7. Functions Check out our R Playlist: https://goo.gl/huUh7Y Subscribe to our channel to get video updates. Hit the subscribe button above. #R #Rtutorial #Ronlinetraining #Rforbeginners #Rprogramming How it Works? 1. This is a 5 Week Instructor led Online Course, 30 hours of assignment and 20 hours of project work 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will be working on a real time project for which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - - - - About the Course Edureka's Data Analytics with R training course is specially designed to provide the requisite knowledge and skills to become a successful analytics professional. It covers concepts of Data Manipulation, Exploratory Data Analysis, etc before moving over to advanced topics like the Ensemble of Decision trees, Collaborative filtering, etc. During our Data Analytics with R Certification training, our instructors will help you: 1. Understand concepts around Business Intelligence and Business Analytics 2. Explore Recommendation Systems with functions like Association Rule Mining , user-based collaborative filtering and Item-based collaborative filtering among others 3. Apply various supervised machine learning techniques 4. Perform Analysis of Variance (ANOVA) 5. Learn where to use algorithms - Decision Trees, Logistic Regression, Support Vector Machines, Ensemble Techniques etc 6. Use various packages in R to create fancy plots 7. Work on a real-life project, implementing supervised and unsupervised machine learning techniques to derive business insights - - - - - - - - - - - - - - - - - - - Who should go for this course? This course is meant for all those students and professionals who are interested in working in analytics industry and are keen to enhance their technical skills with exposure to cutting-edge practices. This is a great course for all those who are ambitious to become 'Data Analysts' in near future. This is a must learn course for professionals from Mathematics, Statistics or Economics background and interested in learning Business Analytics. - - - - - - - - - - - - - - - - Why learn Data Analytics with R? The Data Analytics with R training certifies you in mastering the most popular Analytics tool. "R" wins on Statistical Capability, Graphical capability, Cost, rich set of packages and is the most preferred tool for Data Scientists. Below is a blog that will help you understand the significance of R and Data Science: Mastering R Is The First Step For A Top-Class Data Science Career Having Data Science skills is a highly preferred learning path after the Data Analytics with R training. Check out the upgraded Data Science Course For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free). Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 323025 edureka!
Final Year Projects | Recommendation Method for Improving Customer Lifetime Value
 
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Final Year Projects | Recommendation Method for Improving Customer Lifetime Value More Details: Visit http://clickmyproject.com/a-secure-erasure-codebased-cloud-storage-system-with-secure-data-forwarding-p-128.html Including Packages ======================= * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 93 Clickmyproject
Generating Query Facets using Knowledge Bases
 
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Generating Query Facets using Knowledge Bases To get this project in ONLINE or through TRAINING Sessions, Contact: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83.Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #37, Kamaraj Salai,Thattanchavady, Puducherry -9.Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690, Email: [email protected], web: http://www.jpinfotech.org A query facet is a significant list of information nuggets that explains an underlying aspect of a query. Existing algorithms mine facets of a query by extracting frequent lists contained in top search results. The coverage of facets and facet items mined by this kind of methods might be limited, because only a small number of search results are used. In order to solve this problem, we propose mining query facets by using knowledge bases which contain high-quality structured data. Specifically, we first generate facets based on the properties of the entities which are contained in Freebase and correspond to the query. Second, we mine initial query facets from search results, then expanding them by finding similar entities from Freebase. Experimental results show that our proposed method can significantly improve the coverage of facet items over the state-of-the-art algorithms.
Views: 141 jpinfotechprojects
Building Practical Recommd Engines Part 1: Inst recommenderlab pacg in RStudio | packtpub.com
 
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This playlist/video has been uploaded for Marketing purposes and contains only selective videos. For the entire video course and code, visit [http://bit.ly/2ldq24U]. The recommenderlab R package is a framework for developing and testing recommendation algorithms used to build recommendation engines. In this video, we’ll see how to installrecommenderlab • Install the recommenderlab package • Load the package • See how to get help on recommenderlab For the latest Big Data and Business Intelligence video tutorials, please visit http://bit.ly/1HCjJik Find us on Facebook -- http://www.facebook.com/Packtvideo Follow us on Twitter - http://www.twitter.com/packtvideo
Views: 345 Packt Video
Domain Sensitive Recommendation with User Item Subgroup Analysis
 
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2016 IEEE Transaction on Knowledge and Data Engineering For More Details::Contact::K.Manjunath - 09535866270 http://www.tmksinfotech.com and http://www.bemtechprojects.com 2016 and 2017 IEEE @ TMKS Infotech,Bangalore
Views: 884 manju nath
QueRIE Collaborative Database Exploration
 
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QueRIE Collaborative Database Exploration +91-9994232214,8144199666, [email protected], www.projectsieee.com, www.ieee-projects-chennai.com IEEE PROJECTS 2014 ----------------------------------- Contact:+91-9994232214,+91-8144199666 Email:[email protected] http://ieee.projectsieee.com/Cloud-Computing http://ieee.projectsieee.com/Data-Mining http://ieee.projectsieee.com/Android http://ieee.projectsieee.com/Image-Processing http://ieee.projectsieee.com/Networking http://ieee.projectsieee.com/Network-Security http://ieee.projectsieee.com/Mobile-Computing http://ieee.projectsieee.com/Parallel-Distributed http://ieee.projectsieee.com/Wireless-Communication http://ieee.projectsieee.com/NS2-Projects http://ieee.projectsieee.com/Matlab Support: ------------- Projects Code Documentation PPT Projects Video File Projects Explanation Teamviewer Support
Views: 89 PROJECTS2014