A hybrid collaborative filtering recommender system using a new similarity measure

Authors:
Hyung Jun Ahn
Affiliations:
Management Systems, Waikato Management School, University of Waikato, Hamilton, New Zealand
Venue:
ACOS'07 Proceedings of the 6th Conference on WSEAS International Conference on Applied Computer Science - Volume 6
Year:
2007

Citing 6
Cited 0

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
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
Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering

ACM Transactions on Information Systems (TOIS)
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

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a hybrid recommender system using a new heuristic similarity measure for collaborative filtering that focuses on improving performance under cold-start conditions where only a small number of ratings are available for similarity calculation for each user. The new measure is based on the domain-specific interpretation of rating differences in user data. Experiments using three datasets show the superiority of the measure in new user cold-start conditions.