Attention Mechanism indicating Item Novelty for Sequential Recommendation

Abstract

Most sequential recommendation systems, including those that employ a variety of features and state-of-the-art network models, tend to favor items that are the most popular or of greatest relevance to the historic behavior of the user. Recommendations made under these conditions tend to be repetitive; i.e., many options that might be of interest to users are entirely disregarded. This paper presents a novel algorithm that assigns a novelty score to potential recommendation items. We also present an architecture by which to incorporate this functionality in existing recommendation systems. In experiments, the proposed NASM system outperformed state-of-the-art sequential recommender systems, thereby verifying that the inclusion of novelty score can indeed improve recommendation performance.