User Behavior Modeling (UBM) plays a critical role in user interest learning, and has been
extensively used in recommender systems. The exploration of key interactive patterns …

In this work, we present TriMLP as a foundational MLP-like architecture for the sequential
recommendation, simultaneously achieving computational efficiency and promising …

Click-through rate (CTR) prediction plays a pivotal role in real-world applications,
particularly in recommendation systems and online advertising. A significant research …

Cross-domain recommendation (CDR), which leverages information collected from other
domains, has been empirically demonstrated to effectively alleviate data sparsity and cold …

Sequential recommendation (SR) aims to provide appropriate items a user will click
according to the user's historical behavior sequence. Conventional SR models are trained …

Sequential Recommendation (SR) seeks to anticipate the item that users will interact with at
the next moment, utilizing their historical sequences of interactions. Its core task is to mine …

The fusion of side information in sequential recommendation (SR) is a recommendation
system technique that combines a user's historical behavior sequence with additional side …

In this paper, we present a MLP-like architecture for sequential recommendation, namely
TriMLP, with a novel Triangular Mixer for cross-token communications. In designing …

Sequential recommendation approaches have demonstrated remarkable proficiency in
modeling user preferences. Nevertheless, they are susceptible to profile pollution attacks …

Sequential recommendation methods can capture dynamic user preferences from user
historical interactions to achieve better performance. However, most existing methods only …