As one of the most successful AI-powered applications, recommender systems aim to help
people make appropriate decisions in an effective and efficient way, by providing …

In order to develop effective sequential recommenders, a series of sequence representation
learning (SRL) methods are proposed to model historical user behaviors. Most existing SRL …

Recently, the generality of natural language text has been leveraged to develop transferable
recommender systems. The basic idea is to employ pre-trained language models (PLM) to …

Recently, large language models (LLMs) have shown great potential in recommender
systems, either improving existing recommendation models or serving as the backbone …

Mainstream solutions to sequential recommendation represent items with fixed vectors.
These vectors have limited capability in capturing items' latent aspects and users' diverse …

Session-based Recommendation (SBR) refers to the task of predicting the next item based
on short-term user behaviors within an anonymous session. However, session embedding …

Recently, frequency transformation (FT) has been increasingly incorporated into deep
learning models to significantly enhance state-of-the-art accuracy and efficiency in time …

The goal of sequential recommendation (SR) is to predict a user's potential interested items
based on her/his historical interaction sequences. Most existing sequential recommenders …

The self-attention mechanism, which equips with a strong capability of modeling long-range
dependencies, is one of the extensively used techniques in the sequential recommendation …

In practical recommendation scenarios, users often interact with items under multi-typed
behaviors (eg, click, add-to-cart, and purchase). Traditional collaborative filtering techniques …