Recommender systems are web based systems that aim at predicting a customer's interest on available products and services by relying on previously rated products and dealing with the problem of information and product overload. Collaborative filtering is the most popular recommendation technique nowadays and it mainly employs the user item rating data set. Traditional collaborative filtering ap...

Collaborative filtering has become one of the most used approaches to provide personalized services for users. The key of this approach is to find similar users or items using user-item rating matrix so that the system can show recommendations for users. However, most approaches related to this approach are based on similarity algorithms, such as cosine, Pearson correlation coefficient, and mea...

We present Graph Attention Collaborative Similarity Embedding (GACSE), a new recommendation framework that exploits collaborative information in the user-item bipartite graph for representation learning. Our consists of two parts: first part is to learn explicit filtering such as association through embedding propagation with attention mechanism, and second implicit user-user similarities item-...

This paper presents a hybrid recommender system using a new heuristic similarity measure for collaborative filtering that focuses on improving performance under cold-start conditions where only a small number of ratings are available for similarity calculation for each user. The new measure is based on the domain-specific interpretation of rating differences in user data. Experiments using thre...

Collaborative filtering (CF) is one of the most prevailing and promising approaches in recommender systems. The algorithms precision of collaborative filtering has attracted ever-increasing study of researchers. Traditional user-based approaches for collaborative filtering identify user similarity by analyzing the co-rating items between users and utilize user similarity as predicted weight in ...