GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Fab: content-based, collaborative recommendation
Communications of the ACM
Jester 2.0 (poster abstract): evaluation of an new linear time collaborative filtering algorithm
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Content-based book recommending using learning for text categorization
DL '00 Proceedings of the fifth ACM conference on Digital libraries
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
MovieLens unplugged: experiences with an occasionally connected recommender system
Proceedings of the 8th international conference on Intelligent user interfaces
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
Probabilistic Memory-Based Collaborative Filtering
IEEE Transactions on Knowledge and Data Engineering
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
An Improved Cluster Labeling Method for Support Vector Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
IEEE Transactions on Knowledge and Data Engineering
Using Singular Value Decomposition Approximation for Collaborative Filtering
CEC '05 Proceedings of the Seventh IEEE International Conference on E-Commerce Technology
Unifying user-based and item-based collaborative filtering approaches by similarity fusion
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Dynamic Characterization of Cluster Structures for Robust and Inductive Support Vector Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Support vector machines for collaborative filtering
Proceedings of the 44th annual Southeast regional conference
VCR: Virtual community recommender using the technology acceptance model and the user's needs type
Expert Systems with Applications: An International Journal
A personalized English learning recommender system for ESL students
Expert Systems with Applications: An International Journal
Classification-based collaborative filtering using market basket data
Expert Systems with Applications: An International Journal
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Equilibrium-Based Support Vector Machine for Semisupervised Classification
IEEE Transactions on Neural Networks
Improving memory-based collaborative filtering via similarity updating and prediction modulation
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
Collaborative filtering based on significances
Information Sciences: an International Journal
A literature review and classification of recommender systems research
Expert Systems with Applications: An International Journal
A framework for collaborative filtering recommender systems
Expert Systems with Applications: An International Journal
Incorporating reliability measurements into the predictions of a recommender system
Information Sciences: an International Journal
Knowledge-Based Systems
| Hi-index | 12.06 |
Memory-based collaborative filtering is the state-of-the-art method in recommender systems and has proven to be successful in various applications. In this paper we develop novel memory-based methods that incorporate the level of a user credit instead of using similarity between users. The user credit is the degree of one's rating reliability that measures how adherently the user rates items as others do. Preliminary simulation results show that the proposed methods outperform the conventional memory-based ones. The methods are effective in a cold-starting problem.