In recent years, especially with the (COVID-19) pandemic, shopping has been a challenging
task. Increased online shopping has increased information available via the World Wide …

Recommender systems suggest items of interest to users based on their preferences. These
preferences are typically generated from user ratings of the items. If there are no ratings for a …

User preference modeling in recommendation system aims to improve customer experience
through discovering users' intrinsic preference based on prior user behavior data. This is a …

Recent years have witnessed the explosive growth of interaction behaviors in multimedia
information systems, where multi-behavior recommender systems have received increasing …

Cognitive inspired recommendation systems have attracted increasing attention in recent
years, aiming at fitting user ratings on certain items. However, the performance of …

Social relations are often used as auxiliary information to address data sparsity and cold-
start issues in social recommendations. In the real world, social relations among users are …

Knowledge graph employs the specific graph structure to store knowledge in the form of
entities, relations, attributes, and so forth, which can effectively represent correlations among …

Recommender methods have been effectively used in both academic and industrial settings.
However, the cold start problem with scarce prior information has become the barrier …

This paper reports on the development of a Consistency Regularized model for Bayesian
Personalized Ranking (CR-BPR), addressing to the drawbacks in existing complementary …