GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Pointing the way: active collaborative filtering
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Web-collaborative filtering: recommending music by crawling the Web
Proceedings of the 9th international World Wide Web conference on Computer networks : the international journal of computer and telecommunications netowrking
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Methods and metrics for cold-start recommendations
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
Clustering Approach for Hybrid Recommender System
WI '03 Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
ACM Transactions on Information Systems (TOIS)
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Knowledge and Information Systems
Feature Selection Methods for Conversational Recommender Systems
EEE '05 Proceedings of the 2005 IEEE International Conference on e-Technology, e-Commerce and e-Service (EEE'05) on e-Technology, e-Commerce and e-Service
Incorporating contextual information in recommender systems using a multidimensional approach
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
Collaborative filtering supporting web site navigation
AI Communications
CinemaScreen Recommender Agent: Combining Collaborative and Content-Based Filtering
IEEE Intelligent Systems
Naïve filterbots for robust cold-start recommendations
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Similarity Measure and Instance Selection for Collaborative Filtering
International Journal of Electronic Commerce
A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem
Information Sciences: an International Journal
Making use of associative classifiers in order to alleviate typical drawbacks in recommender systems
Expert Systems with Applications: An International Journal
An improved privacy-preserving DWT-based collaborative filtering scheme
Expert Systems with Applications: An International Journal
A literature review and classification of recommender systems research
Expert Systems with Applications: An International Journal
Information Processing and Management: an International Journal
A New Evolution Mechanism Model for B2B E-Commerce Network
Journal of Electronic Commerce in Organizations
International Journal of Business Information Systems
A new evolution model for B2C e-commerce market
Information Technology and Management
Defending recommender systems by influence analysis
Information Retrieval
Hi-index | 12.06 |
Collaborative filtering (CF) is one of the most widely used methods for personalized product recommendation at online stores. CF predicts users' preferences on products using past data of users such as purchase records or their ratings on products. The prediction is then used for personalized recommendation so that products with highly estimated preference for each user are selected and presented. One of the most difficult issues in using CF is that it is often hard to collect sufficient amount of data for each user to estimate preferences accurately enough. In order to address this problem, this research studies how we can gain the most information about each user by collecting data on a very small number of selected products, and develops a method for choosing a sequence of such products tailored to each user based on metrics from information theory and correlation-based product similarity. The effectiveness of the proposed methods is tested using experiments with the MovieLens dataset.