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On the Effectiveness of Linear Models for One-Class Collaborative Filtering

Suvash Sedhain, Aditya Menon, Scott Sanner, Darius Braziunas
2016 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
A state-of-the-art method for such implicit feedback, or one-class collaborative filtering (OC-CF), problems is SLIM, which makes recommendations based on a learned item-item similarity matrix.  ...  Our results thus illustrate that the OC-CF problem can be effectively tackled via linear classification models.  ...  The authors thank the anonymous reviewers for their valuable feedback.  ... 
doi:10.1609/aaai.v30i1.9991 fatcat:i44ojgfcfjashmrfstadxbyggq

Unified filtering by combining collaborative filtering and content-based filtering via mixture model and exponential model

Luo Si, Rong Jin
2004 Proceedings of the Thirteenth ACM conference on Information and knowledge management - CIKM '04  
Collaborative filtering and content-based filtering are two types of information filtering techniques. Combining these two techniques can improve the recommendation effectiveness.  ...  Experiments have shown that the new unified filtering algorithm outperforms a pure collaborative filtering approach, a pure content-based filtering approach and another unified filtering algorithm.  ...  There have been a couple of studies on combining content based filtering and collaborative filtering [2, 4] .  ... 
doi:10.1145/1031171.1031201 dblp:conf/cikm/SiJ04 fatcat:el6ihdt7b5dadflzhgejdqyimy

Improving one-class collaborative filtering by incorporating rich user information

Yanen Li, Jia Hu, ChengXiang Zhai, Ye Chen
2010 Proceedings of the 19th ACM international conference on Information and knowledge management - CIKM '10  
Experimental results on a largescale retail data set from a major e-commerce company show that the proposed methods are effective and can improve the performance of the One-Class Collaborative Filtering  ...  One-Class Collaborative Filtering (OCCF) is an emerging setup in collaborative filtering in which only positive examples or implicit feedback can be observed.  ...  We also thank Huizhong Duan from the University of Illinois at Urbana-Champaign and Maks Ovsjanikov from Stanford University for their help in the data analysis and discussion.  ... 
doi:10.1145/1871437.1871559 dblp:conf/cikm/LiHZC10 fatcat:tlqef5g22rgulmezl7537uokqq

Collaborative filtering or regression models for Internet recommendation systems?

Andreas Mild, Martin Natter
2002 Journal of Targeting, Measurement and Analysis for Marketing  
For a large number of customers and movies, it is shown that simple linear regression with model selection can provide significantly better recommendations than collaborative filtering.  ...  Two commonly used collaborative filtering approaches are compared with several regression models using an experimental full factorial design.  ...  Acknowledgment The authors would like to express their gratitude to Kerry Barner and two anonymous referees for the valuable comments and suggestions on a previous version of this paper.  ... 
doi:10.1057/palgrave.jt.5740055 fatcat:wgdhfanv6fh3fjyuwc6tzmcvmq

Collaborative Filtering with the Simple Bayesian Classifier [chapter]

Koji Miyahara, Michael J. Pazzani
2000 Lecture Notes in Computer Science  
They recommend items to a user based on the opinions of other users with similar tastes. In this paper, we discuss an approach to collaborative filtering based on the simple Bayesian classifier.  ...  In this paper, we define two variants of the recommendation problem for the simple Bayesian classifier.  ...  Collaborative Filtering The main idea of collaborative filtering is to recommend new items of interest for a particular user based on other users' opinions.  ... 
doi:10.1007/3-540-44533-1_68 fatcat:fgwa6ldjqfderepb6gcmnohuy4

Collaborative online learning of user generated content

Guangxia Li, Kuiyu Chang, Steven C.H. Hoi, Wenting Liu, Ramesh Jain
2011 Proceedings of the 20th ACM international conference on Information and knowledge management - CIKM '11  
Experimental results show that our method is effective and scalable for timely classification of user generated content.  ...  On the other hand, a personalized model dedicated to each user may be inaccurate due to the scarcity of training data, especially at the very beginning; when users have written just a few articles.  ...  Spam Email Filtering We apply online collaborative learning to construct effective personalized spam email filters.  ... 
doi:10.1145/2063576.2063622 dblp:conf/cikm/LiCHLJ11 fatcat:gxg3mvcy4rcg7oljznhg6kgeay

Online multi-person tracking via robust collaborative model

Mohamed A. Naiel, M. Omair Ahmad, M. N. S. Swamy, Yi Wu, Ming-Hsuan Yang
2014 2014 IEEE International Conference on Image Processing (ICIP)  
In this paper, we present a model for collaboration between a pre-trained object detector and multiple single object trackers in the particle filter tracking framework.  ...  The performance of the proposed algorithm compares favorably with that of the state-of-the-art approaches on three public sequences.  ...  Fig. 1 . 1 Effect of changing the collaborative factor γ. Fig. 2 . 2 Effect of the proposed collaborative model on the tracker particles.  ... 
doi:10.1109/icip.2014.7025086 dblp:conf/icip/NaielASWY14 fatcat:w3ewneqtoncg7b3gb5j4pnw45y

Constructing the Structure of Utility Graphs Used in Multi-Item Negotiation Through Collaborative Filtering of Aggregate Buyer Preferences [chapter]

Valentin Robu, Han La Poutré
2008 Studies in Computational Intelligence  
Our method is based on techniques inspired from item-based collaborative filtering, widely used in online recommendation algorithms.  ...  This chapter considerably extends the results of our previous work [14] , by proposing a method for constructing the utility graphs of buyers automatically, based on previous negotiation data.  ...  built through collaborative filtering.  ... 
doi:10.1007/978-3-540-76282-9_9 fatcat:pxt2vvxffnd2njkr6l32bm6i7a

A Hybrid Course Recommendation System by Integrating Collaborative Filtering and Artificial Immune Systems

Pei-Chann Chang, Cheng-Hui Lin, Meng-Hui Chen
2016 Algorithms  
This research proposes a two-stage user-based collaborative filtering process using an artificial immune system for the prediction of student grades, along with a filter for professor ratings in the course  ...  We therefore test the effects of filtering out courses where the instructors scored poorly in feedback evaluation, on our prediction matrix to observe the effects on prediction accuracy.  ...  We successfully demonstrate the effectiveness of employing the collaborative model and show that the use of a quality filter for professor ratings does not interfere with the predictions.  ... 
doi:10.3390/a9030047 fatcat:jypekg53bfcofgq4di4jvkzxf4

Variational Autoencoders for Collaborative Filtering

Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman, Tony Jebara
2018 Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18  
This non-linear probabilistic model enables us to go beyond the limited modeling capacity of linear factor models which still largely dominate collaborative filtering research.  ...  We extend variational autoencoders (vaes) to collaborative filtering for implicit feedback.  ...  In most of the cases, non-linear models (Mult-vae pr , Multdae, and cdae) prove to be more powerful collaborative filtering models than state-of-the-art linear models.  ... 
doi:10.1145/3178876.3186150 dblp:conf/www/LiangKHJ18 fatcat:baidkwo2kvaldh3mr4meqlbxaa

Variational Autoencoders for Collaborative Filtering [article]

Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman, Tony Jebara
2018 arXiv   pre-print
This non-linear probabilistic model enables us to go beyond the limited modeling capacity of linear factor models which still largely dominate collaborative filtering research.We introduce a generative  ...  We extend variational autoencoders (VAEs) to collaborative filtering for implicit feedback.  ...  In most of the cases, non-linear models (Mult-vae pr , Multdae, and cdae) prove to be more powerful collaborative filtering models than state-of-the-art linear models.  ... 
arXiv:1802.05814v1 fatcat:qtdx2jcdfvdbjmfdtprcjxwasi

A New Similarity Measure for User-based Collaborative Filtering in Recommender Systems

T. Srikanth, M. Shashi
2015 INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY  
Collaborative filtering is a popular approach in recommender Systems that helps users in identifying the items they may like in a wagon of items.  ...  Finding similarity among users with the available item ratings so as to predict rating(s) for unseen item(s) based on the preferences of likeminded users for the current user is a challenging problem.  ...  Acknowledgement We Thank MoviLens,Iris and Wine dataset providers for making them available on the web.  ... 
doi:10.24297/ijct.v14i9.1851 fatcat:ol3r3mx4nzea5kepridvig3a5i

Symptom Recommendation using Collaborative Filtering and Disease Prediction using Support Vector Machine

Akshay Kamath, Amogh Parab, Neeraj Kerkar
2018 International Journal of Computer Applications  
The results demonstrate the effectiveness of different machine learning techniques on the given data.  ...  Another machine learning technique for classification is discussed which is used predict the possibility of having a disease.  ...  Rajni Pamnani, who is an assistant professor at KJ Somaiya College of Engineering, for lending her support and expertise, which were instrumental in giving our project a basis and direction.  ... 
doi:10.5120/ijca2018916977 fatcat:nz24hq3nxjgivifkpqpjz2ne7i

Developing an intelligent trip recommender system by data mining methods

Tamer Uçar
2016 Global Journal of Information Technology Emerging Technologies  
Tourism domain is one of these sectors.  ...  This study proposes an implementation of an expert system framework which can accurately classify users and make predictions about user classifications for recommending tourism related services.  ...  mining model for prediction of user classes. (4) Predicting class for a target user. (5) Recommending services suitable for predicted user class.  ... 
doi:10.18844/gjit.v6i1.398 fatcat:ydw3qhwnq5exffntsmdntxqn4e

Disease Risk Prediction using SVM based on Geographical Location

Abarna A R, A Umamakeswari
2018 International Journal of Engineering & Technology  
The experimental output shows that the proposed method is more effective when compared with Collaborative Filtering based Disease Risk Assessment.  ...  The proposed method specifies the assessment of disease risk by Support Vector Machine (SVM) algorithm to identify the similarity between the users based on the geographical location and then recommends  ...  It can be used for classification as well as regression. SVM works on non-linear and linear feature.  ... 
doi:10.14419/ijet.v7i2.24.11989 fatcat:j7w4gfe4qffvtkdy77atemt6ni
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