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Sequential recommender systems have shown effective suggestions by capturingusers' interest drift. There have been two groups of existing sequentialmodels: user- and item-centric models. The user-centric models capturepersonalized interest drift based on each user's sequential consumptionhistory, but do not explicitly consider whether users' interest in itemssustains beyond the training time, i.e., interest sustainability. On the otherhand, the item-centric models consider whether users' general interest sustainsafter the training time, but it is not personalized. In this work, we propose arecommender system taking advantages of the models in both categories. Ourproposed model captures personalized interest sustainability, indicatingwhether each user's interest in items will sustain beyond the training time ornot. We first formulate a task that requires to predict which items each userwill consume in the recent period of the training time based on users'consumption history. We then propose simple yet effective schemes to augmentusers' sparse consumption history. Extensive experiments show that the proposedmodel outperforms 10 baseline models on 11 real-world datasets. The codes areavailable at https://github.com/dmhyun/PERIS.

Beyond Learning from Next Item: Sequential Recommendation via Personalized Interest Sustainability