Modern recommender systems often embed users and items into low-dimensional latent
representations, based on their observed interactions. In practical recommendation …

Session-based recommendation is a challenging task. Without access to a user's historical
user-item interactions, the information available in an ongoing session may be very limited …

Large language models (LLMs) have not only revolutionized the field of natural language
processing (NLP) but also have the potential to bring a paradigm shift in many other fields …

Abstract In the Big Data Era, recommender systems perform a fundamental role in data
management and information filtering. In this context, Collaborative Filtering (CF) persists as …

Recently, user cold-start recommendations have attracted a lot of attention from industry and
academia. In user cold-start recommendation systems, the user attribute information is often …

In an era dominated by information overload, effective recommender systems are essential
for managing the deluge of data across digital platforms. Multi-stage cascade ranking …

Recently, recommender systems have achieved promising performances and become one
of the most widely used web applications. However, recommender systems are often trained …

Different from the traditional recommender system, the session-based recommender system
introduces the concept of the session, ie, a sequence of interactions between a user and …

Recommendation services become a critical and hot research topic for researchers. A
recommendation agent that automatically suggests products to users according to their …

Session-based recommendation (SBR), which mainly relies on a user's limited interactions
with items to generate recommendations, is a widely investigated task. Existing methods …