Recent recommendation systems have achieved good results by applying Graph Neural
Network (GNN) to user-item interaction graphs. However, these recommendation systems …

Recommender system uses user-item historical interactions to portray user preferences.
Due to the problem of data sparseness, auxiliary information is introduced to describe user …

Review text is a valuable source of information for recommendation systems and often
contains rich semantics with user preferences and item attributes. Recently, mainstream …

This paper introduces CM-DIR (Conceptual Model-Dynamicity and Interaction in
Recommender Systems), a method that we have originally devised to help with the …

Recommendation systems were developed to predict the users' preferences by tracking the
interactions between users and items. While the interaction history and the basic information …

In recent years, the term “big data” has attracted the attention of many scholars and business
managers, and the emergence of massive data has ushered in a major transformation of the …

Massive information often make users lose their focuses so that desired targets are missed.
Recommendation system can efficiently provide interesting information for users in the case …

This paper introduces CM-DIR (Conceptual Model Dynamicity and Interaction in
Recommender Systems), a method that we have originally devised to help with the …