Paper: Relevance Feedback Models For Recommendation

ACL ID W06-1653
Title Relevance Feedback Models For Recommendation
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2006
  • Masao Utiyama (National Institute of Information and Communications Technology, Kyoto Japan)
  • Mikio Yamamoto (University of Tsukuba, Tsukuba Japan)

We extended language modeling ap- proaches in information retrieval (IR) to combine collaborative filtering (CF) and content-based filtering (CBF). Our ap- proach is based on the analogy between IR and CF, especially between CF and rel- evance feedback (RF). Both CF and RF exploit users’ preference/relevance judg- ments to recommend items. We first in- troduce a multinomial model that com- bines CF and CBF in a language modeling framework. We then generalize the model to another multinomial model that approx- imates the Polya distribution. This gener- alized model outperforms the multinomial model by 3.4% for CBF and 17.4% for CF in recommending English Wikipedia articles. The performance of the gener- alized model for three different datasets was comparable to that of a state-of-the- ar...