Paper: Well-Argued Recommendation: Adaptive Models Based on Words in Recommender Systems

ACL ID D13-1200
Title Well-Argued Recommendation: Adaptive Models Based on Words in Recommender Systems
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2013
Authors

Recommendation systems (RS) take advan- tage of products and users information in order to propose items to consumers. Collaborative, content-based and a few hybrid RS have been developed in the past. In contrast, we propose a new domain-independent semantic RS. By providing textually well-argued recommenda- tions, we aim to give more responsibility to the end user in his decision. The system includes a new similarity measure keeping up both the accuracy of rating predictions and coverage. We propose an innovative way to apply a fast adaptation scheme at a semantic level, provid- ing recommendations and arguments in phase with the very recent past. We have performed several experiments on films data, providing textually well-argued recommendations.