Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Horting hatches an egg: a new graph-theoretic approach to collaborative filtering
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Recommending from content: preliminary results from an e-commerce experiment
CHI '00 Extended Abstracts on Human Factors in Computing Systems
Learning with Genetic Algorithms: An Overview
Machine Learning
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Learning Collaborative Information Filters
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Using Collaborative Filtering Data in Case-Based Recommendation
Proceedings of the Fifteenth International Florida Artificial Intelligence Research Society Conference
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
IEEE Transactions on Knowledge and Data Engineering
WI '05 Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence
IEEE Transactions on Knowledge and Data Engineering
Generating semantically enriched user profiles for Web personalization
ACM Transactions on Internet Technology (TOIT)
Modeling relationships at multiple scales to improve accuracy of large recommender systems
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Private distributed collaborative filtering using estimated concordance measures
Proceedings of the 2007 ACM conference on Recommender systems
A recursive prediction algorithm for collaborative filtering recommender systems
Proceedings of the 2007 ACM conference on Recommender systems
A Compact User Model for Hybrid Movie Recommender System
ICCIMA '07 Proceedings of the International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007) - Volume 01
Applications of wavelet data reduction in a recommender system
Expert Systems with Applications: An International Journal
Fuzzy-genetic approach to recommender systems based on a novel hybrid user model
Expert Systems with Applications: An International Journal
Designing Specific Weighted Similarity Measures to Improve Collaborative Filtering Systems
ICDM '08 Proceedings of the 8th industrial conference on Advances in Data Mining: Medical Applications, E-Commerce, Marketing, and Theoretical Aspects
A random walk method for alleviating the sparsity problem in collaborative filtering
Proceedings of the 2008 ACM conference on Recommender systems
Tutorial on recent progress in collaborative filtering
Proceedings of the 2008 ACM conference on Recommender systems
Fuzzy computational models for trust and reputation systems
Electronic Commerce Research and Applications
ItemRank: a random-walk based scoring algorithm for recommender engines
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
A probabilistic definition of item similarity
Proceedings of the fifth ACM conference on Recommender systems
Incorporating fuzzy trust in collaborative filtering based recommender systems
SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part I
A novel mobile recommender system for indoor shopping
Expert Systems with Applications: An International Journal
Incorporating reliability measurements into the predictions of a recommender system
Information Sciences: an International Journal
A balanced memory-based collaborative filtering similarity measure
International Journal of Intelligent Systems
Attribute-based collaborative filtering using genetic algorithm and weighted C-means algorithm
International Journal of Business Information Systems
International Journal of Business Information Systems
A new user similarity model to improve the accuracy of collaborative filtering
Knowledge-Based Systems
An entropy-based neighbor selection approach for collaborative filtering
Knowledge-Based Systems
| Hi-index | 12.05 |
Collaborative filtering is a popular recommendation technique, which suggests items to users by exploiting past user-item interactions involving affinities between pairs of users or items. In spite of their huge success they suffer from a range of problems, the most fundamental being that of data sparsity. When the rating matrix is sparse, local similarity measures yield a poor neighborhood set thus affecting the recommendation quality. In such cases global similarity measures can be used to enrich the neighborhood set by considering transitive relationships among users even in the absence of any common experiences. In this work we propose a recommender system framework utilizing both local and global similarities, taking into account not only the overall sparsity in the rating data, but also sparsity at the user-item level. Several schemes are proposed, based on various sparsity measures pertaining to the active user, for the estimation of the parameter @a, that allows the variation of the importance given to the global user similarity with regards to local user similarity. Furthermore, we propose an automatic scheme for weighting the various sparsity measures, through evolutionary approach, to obtain a unified measure of sparsity (UMS). In order to take maximum possible advantage of the various sparsity measures relating to an active user, a scheme based on the UMS is suggested for estimating @a. Experimental results demonstrate that the proposed estimates of @a, markedly, outperform the schemes for which @a is kept constant across all predictions (fixed-@a schemes), on accuracy of predicted ratings.