Abstract. Implicit acquisition of user preferences makes log-based collaborative filtering favorable in practice to accomplish recommendations. In this paper, we follow a formal ap...
Jun Wang, Arjen P. de Vries, Marcel J. T. Reinders
In this paper we propose a model for relevance feedback. Our model combines evidence from user's relevance assessments with algorithms describing how words are used within do...
Recommender systems are intelligent applications that help on-line users to tackle information overload by providing recommendations of relevant items. Collaborative Filtering (CF...
Conventional conversational recommender systems support interaction strategies that are hard-coded into the system in advance. In this context, Reinforcement Learning techniques h...
Pseudo-relevance feedback (PRF) improves search quality by expanding the query using terms from high-ranking documents from an initial retrieval. Although PRF can often result in ...
Marc-Allen Cartright, James Allan, Victor Lavrenko...