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Sequential Recommendation (SRs) that capture users' dynamic intents bymodeling user sequential behaviors can recommend closely accurate products tousers. Previous work on SRs is mostly focused on optimizing the recommendationaccuracy, often ignoring the recommendation diversity, even though it is animportant criterion for evaluating the recommendation performance. Mostexisting methods for improving the diversity of recommendations are not ideallyapplicable for SRs because they assume that user intents are static and rely onpost-processing the list of recommendations to promote diversity. We considerboth recommendation accuracy and diversity for SRs by proposing an end-to-endneural model, called Intent-aware Diversified Sequential Recommendation (IDSR).Specifically, we introduce an Implicit Intent Mining module (IIM) into SRs tocapture different user intents reflected in user behavior sequences. Then, wedesign an Intent-aware Diversity Promoting (IDP) loss to supervise the learningof the IIM module and force the model to take recommendation diversity intoconsideration during training. Extensive experiments on two benchmark datasetsshow that IDSR significantly outperforms state-of-the-art methods in terms ofrecommendation diversity while yielding comparable or superior recommendationaccuracy.

Improving End-to-End Sequential Recommendations with Intent-aware Diversification