A hybrid collaborative filtering recommender system using a new similarity measure
Hyung Jun Ahn · ACOS'07 Proceedings of the 6th Conference on WSEAS International Conference on Applied Computer Science - Volume 6 · 2007
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.