Recommender systems are an essential tool to relieve the information overload challenge
and play an important role in people’s daily lives. Since recommendations involve allocations …

Explainability and effectiveness are two key aspects for building recommender systems.
Prior efforts mostly focus on incorporating side information to achieve better recommendation …

Traditional studies on recommender systems usually leverage only one type of user behaviors
(the optimization target, such as purchase), despite the fact that users also generate a …

Collaborative filtering (CF) plays a critical role in the development of recommender systems.
Most CF methods utilize an encoder to embed users and items into the same representation …

Traditional recommender systems mainly aim to model inherent and long-term user
preference, while dynamic user demands are also of great importance. Typically, a historical …

The research field of Information Retrieval (IR) has evolved significantly, expanding beyond
traditional search to meet diverse user information needs. Recently, Large Language …

Legal case retrieval is a specialized IR task that involves retrieving supporting cases given
a query case. Compared with traditional ad-hoc text retrieval, the legal case retrieval task is …

Recent studies on recommendation have largely focused on exploring state-of-the-art
neural networks to improve the expressiveness of models, while typically apply the Negative …

Many previous studies attempt to utilize information from other domains to achieve better
performance of recommendation. Recently, social information has been shown effective in …

Sequential recommendation has become a trending research topic for its capability to
capture dynamic user intents based on historical interaction sequence. To train a sequential …