Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Communications of the ACM
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem
Information Sciences: an International Journal
Unified relevance models for rating prediction in collaborative filtering
ACM Transactions on Information Systems (TOIS)
Re-considering neighborhood-based collaborative filtering parameters in the context of new data
Proceedings of the 17th ACM conference on Information and knowledge management
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Using past-prediction accuracy in recommender systems
Information Sciences: an International Journal
Being confident about the quality of the predictions in recommender systems
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
An entropy-based neighbor selection approach for collaborative filtering
Knowledge-Based Systems
| Hi-index | 0.00 |
In this paper a new proposal for memory-based Collaborative Filtering algorithms is presented. In order to compute its recommendations, a first step in memory-based methods is to find the neighborhood for the active user. Typically, this process is carried out by considering a vector-based similarity measure over the users' ratings. This paper presents a new similarity criteria between users that could be used to both neighborhood selection and prediction processes. This criteria is based on the idea that if a user was good predicting the past ratings for the active user, then his/her predictions will be also valid in the future. Thus, instead of considering a vector-based measure between given ratings, this paper shows that it is possible to consider a distance between the real ratings (given by the active user in the past) and the ones predicted by a candidate neighbor. This distance measures the quality of each candidate neighbor at predicting the past ratings. The best-N predictors will be selected as the neighborhood.