Sequential recommendation via agent-based irrelevancy skipping.

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Sequential Recommendation is based on modelling sequential dependencies in user interactions to produce subsequent recommendation results. However, due to the diversity of users' interests and the uncertainty of their behaviours, not all historical interactions in users' interaction sequences are relevant to their next-interaction intents, which hinders generating accurate sequential recommendations. To this end, a novel Sequential Recommendation method, Dynamic-Skip for Sequential Recommendation (DyS4Rec), is proposed in this study. Specifically, by a Long-Short Term Memory (LSTM) with dynamic skip connections, allows DyS4Rec to skip irrelevant interactions to more accurately capture long-term dependencies, which are related to users' next-interaction intents. Furthermore, a Personalized Module (PM) is designed to guide the skipping process and add more personalization to the recommendation results. In this way, DyS4Rec can adaptively learn to exclude the impact of irrelevant historical interactions to precisely model users' personalized interaction intents and generate more accurate sequential recommendations. Extensive experiments on five public real-world datasets (containing items ranging from a few thousand to hundreds of thousands) showcase that DyS4Rec outperforms other state-of-the-art counterparts (by 1% to 12%). Moreover, visualization analyses demonstrate that DyS4Rec can indeed perform meaningful jumps in modelling user interactions to exclude the influence of irrelevant historical interactions and generate more accurate sequential recommendations.