Recommender systems play a crucial role in tackling the challenge of information overload
by delivering personalized recommendations based on individual user preferences. Deep …

Multi-behavioral recommender systems have emerged as a solution to address data sparsity
and cold-start issues by incorporating auxiliary behaviors alongside target behaviors …

In recent years, graph neural networks (GNNs), as the state-of-the-art technology have
advanced the solutions of recommender systems (RSs). However, the representations of …

Multi-behavior recommendations effectively integrate various types of behaviors and have
been proven to enhance recommendation performance. However, existing researches …

Recommender systems have emerged as an indispensable mean to meet personalized
interests of users and alleviate information overload. Despite the great success, accuracy …

In recommender systems, multi-behavior methods have demonstrated their effectiveness in
mitigating issues like data sparsity, a common challenge in traditional single-behavior …

Multi-behavior recommendation systems enhance effectiveness by leveraging auxiliary
behaviors (such as page views and favorites) to address the limitations of traditional models …

While recommender systems with multi-modal item representations (image, audio, and text),
have been widely explored, learning recommendations from multi-modal user interactions …

Compared to traditional single-behavior models, multibehavior recommendation models
incorporate the auxiliary behavior information of users. This integration step addresses the …

While recommender systems with multi-modal item representations (image, audio, and text),
have been widely explored, learning recommendations from multi-modal user interactions …