Multi-interest Diversification for End-to-end Sequential Recommendation.

A preliminary version of this article appeared in the proceedings of CIKM 2020 [10]. In this extension, we (1) propose another interest extractor, i.e., dynamic routing, in the implicit interest mining module, and another type of disagreement regularization, i.e., output disagreement regularization, in our interest-aware, diversity promoting loss; (2) investigate the performance of our multi-interest, diversified, sequential recommendation model with different interest extractors in implicit interest mining, i.e., multi-head attention vs. dynamic routing; (3) investigate the performance of multi-interest, diversified, sequential recommendation with various latent interests numbers; (4) explore the influence of the parameter λ on the performance of multi-interest, diversified, sequential recommendation; (5) investigate the performance of multiinterest, diversified, sequential recommendation with different types of disagreement regularization; (6) investigate the impact of maximum entropy regularization on the performance of multi-interest, diversified, sequential recommendation; (7) provide a case study to show the recommendations generated by multi-interest, diversified, sequential recommendation; (8) analyze the computational complexity of the baseline model as well as our proposal; and (9) survey more related work and conduct a more detailed analysis of the approach and experimental results. [ABSTRACT FROM AUTHOR]