Interactive Graph Propagation Model for Top-N Recommendation

This paper proposes an interactive graph propagation model for Top-N recommendation, in which the user graph propagation and item graph propagation in the 'user-item' rating space are well unified in an interactive way. Considering the imbalance between the original ratings given by user and those predicted ones obtained by graph propagation, we propose an integrated method to measure the similarity between graph vertices. Furthermore, to boost the potential of forming more reliable predicted ratings on items to be recommended for active user, the rating predictions via user graph propagation and item graph propagation are lineally combined. Experimental results on MovieLens and EachMovie data sets demonstrate that the proposed method outperforms the states of the user graph model and item graph model a lot.