Evaluating Design Choices in Tripartite Graph-Based Recommender Systems to Improve Long Tail Recommendations

Abstract

Even though the abaility to recommend items in the long tail is one of the main strengths of recommendation systems, modern models still show decreased performance when recommending these niche items. Various bipartite and tripartite graph-based models have been proposed that are specifically tailored to solving this long tail issue. This study aims to investigate the effect of the design of the additional layer introduced by tripartite graph-based recommender systems on their performance. All options available in the MovieLens 1M dataset are evaluated on recall and diversity. Experimental results suggest that tripartite graphs based on latent information describing the users perform better than ones utilising item-based latent information, but both these options hardly outperform the baseline bipartite model. Regardless of the graph used, normalising the transition matrix is found to significantly increase performance. It is hypothesised that larger user-focused additional layers show increased diversity over smaller options when normalised. Issues regarding the reproducibility of previous research are identified and addressed, and the development of unified evaluation metrics is advocated to prevent such problems in the future.