Core Interests Focused Self-attention for Sequential Recommendation

Abstract

Recent studies identify that sequential recommender systems (SRSs) are improved by self-attention mechanism due to its ability to capture the correlation between interactions. However, two major limitations remain unaddressed by existing works. First, user behaviors in long sequences contain many implicit and noisy preference signals that cannot sufficiently reflect users’ actual preferences. Therefore, modeling all the interactive behaviors will worsen the representation of their actual interests. Second, most models only consider the interaction histories as ordered sequences, while ignoring the time interval between interactions, which leads to a loss of effective information. To tackle these issues, we herein propose CIFARec (Core Interests Focused self-Attention based sequential recommendation), which can explicitly extract those interest-relevant interactions from users’ implicit feedback information and focus on users’ core interests adaptively. Meanwhile, our model takes into account time intervals to retain valid information. Extensive experiments on five benchmark datasets show that our model outperforms various state-of-the-art sequential models consistently.