Beyond Learning from Next Item: Sequential Recommendation via Personalized Interest Sustainability
This work addresses the challenge of improving recommendation accuracy for users in sequential systems by combining user- and item-centric approaches, though it appears incremental as it builds on existing models.
The paper tackles the problem of sequential recommendation by proposing a model that captures personalized interest sustainability, predicting whether a user's interest in items will sustain beyond training time, and it outperforms 10 baseline models on 11 real-world datasets.
Sequential recommender systems have shown effective suggestions by capturing users' interest drift. There have been two groups of existing sequential models: user- and item-centric models. The user-centric models capture personalized interest drift based on each user's sequential consumption history, but do not explicitly consider whether users' interest in items sustains beyond the training time, i.e., interest sustainability. On the other hand, the item-centric models consider whether users' general interest sustains after the training time, but it is not personalized. In this work, we propose a recommender system taking advantages of the models in both categories. Our proposed model captures personalized interest sustainability, indicating whether each user's interest in items will sustain beyond the training time or not. We first formulate a task that requires to predict which items each user will consume in the recent period of the training time based on users' consumption history. We then propose simple yet effective schemes to augment users' sparse consumption history. Extensive experiments show that the proposed model outperforms 10 baseline models on 11 real-world datasets. The codes are available at https://github.com/dmhyun/PERIS.