Context-aware recommendation via graph-based contextual modeling and postfiltering

Abstract

Context-aware recommender systems generate more relevant recommendations by adapting them to the specific contextual situation of the user and have become one of the most active research areas in the recommender systems. However, there remains a key issue as how contextual information can be used to create intelligent and useful recommender systems. To assist the development and use of context-aware recommendation capabilities, we propose a graph-based framework to model and incorporate contextual information into the recommendation process in an advantageous way. A contextual graph-based relevance measure (CGR) is specifically designed to assess the potential relevance between the target user and the items further used to make an item recommendation. We also propose a probabilistic-based postfiltering strategy to refine the recommendation results as contextual conditions are explicitly given in a query. Depending on the experimental results on the two datasets, the CGR-based method is much superior to the traditional collaborative filtering methods, and the proposed postfiltering method is much effective in context-aware recommendation scenario.