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 this https URL.

本文提出了一种推荐系统，结合个性化的兴趣变化和项目的普遍兴趣，通过预测用户在最近时间内消费的项目，捕捉个性化的兴趣可持续性，扩充用户稀疏消费历史数据，并在 11 个真实数据集上优于 10 个基础模型。