Recommender Systems (RSs), which generate personalized content, have become a
technological tool with diverse applications for users. While numerous RSs have been …

The increase in online information and the expanding diversity of user preferences require
developing improved recommender systems. Cross-domain recommender systems (CDRS) …

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 …

A practical recommender system should be able to handle heterogeneous behavioral
feedback as inputs and has multi-task outputs ability. Although the heterogeneous one-class …

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

The recommendation system is an effective approach to alleviate the information overload
caused by the popularization of the Internet. Existing recommendation methods often use …

Research on variational autoencoders for collaborative filtering is gradually focusing on
implicit feedback. However, most existing studies have two limitations:(1) they overlook the …

Collaborative filtering recommends potentially interesting content to users based on
historical data that it collects from users, which can lead to privacy breaches by untrusted …

The purpose of the recommendation systems (RS) is to provide personalized information
filtering for users by analyzing their interactions with items. However, the sparsity of data in …

Multi-modal data presents a promising opportunity for improving multimedia
recommendation models, but it also introduces task-irrelevant noise that can reduce model …