Graph neural network (GNN) based algorithms have achieved superior performance in
recommendation tasks due to their advanced capability of exploiting high-order connectivity …

Existing research efforts for multi-interest candidate matching in recommender systems
mainly focus on improving model architecture or incorporating additional information …

Sequential recommendation is one of the most important tasks in recommender systems,
which aims to recommend the next interacted item with historical behaviors as input …

The retrieval phase is a vital component in recommendation systems, requiring the model to
be effective and efficient. Recently, generative retrieval has become an emerging paradigm …

The pre-training paradigm, ie, learning universal knowledge across a wide spectrum of
domains, has increasingly become a new de-facto practice in many fields, especially for …

The sequential recommendation task based on the multi-interest framework aims to model
multiple interests of users from different aspects to predict their future interactions. However …

Text matching systems have become a fundamental service in most Searching platforms.
For instance, they are responsible for matching user queries to relevant candidate items, or …

Recently, multi-interest models, which extract interests of a user as multiple representation
vectors, have shown promising performances for sequential recommendation. However …

Multi-interest recommendation methods extract multiple interest vectors to represent the user
comprehensively. Despite their success in the matching stage, previous works overlook the …

Personalized recommender systems aim to predict users' preferences for items. It has
become an indispensable part of online services. Online social platforms enable users to …