User Behavior Learning in Designing Restaurant Recommender Systems: An Approach to Leveraging Historical Data and Implicit Feedback

Abstract

In typical restaurant recommendations, knowledge-based methods are used most often and do not take advantage of personal historical data. In this thesis, we are going to make some improvements to the Chicago Entrée restaurant recommender system. We will exploit the historical data and propose a weighted similarity approach to combine heuristic similarity with tag similarity between restaurants. Also, we show an improved way to mine the semantics of user behaviors using heuristic metric. These proposed approaches are evaluated by the comparison of three different pairwise approaches to learning to rank (LTR) in matrix factorization and five classic recommendation algorithms. The result shows that the combinatorial similarity outperforms the heuristic similarity on the precision, recall, F-score, and mean reciprocal rank.