Performance Prediction in Recommender Systems

Research on Recommender Systems has barely explored the issue of adapting a recommendation strategy to the user’s information available at a certain time. In this thesis, we introduce a component that allows building dynamic recommendation strategies, by reformulating the performance prediction problem in the area of Information Retrieval to that of recommender systems. More specifically, we investigate a number of adaptations of the query clarity predictor in order to infer the ambiguity in user and item profiles. The properties of each predictor are empirically studied by, first, checking the correlation of the predictor output with a performance measure, and second, by incorporating a performance predictor into a recommender system to produce a dynamic strategy. Depending on how the predictor is integrated with the system, we explore two different applications: dynamic user neighbour weighting and hybrid recommendation. The performance of such dynamic strategies is examined and compared with that of static ones.