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Interactions of users and items can be naturally modeled as a user-item bipartite graph in recommender systems, and emerging research is devoted to exploring user-item graphs for collaborative filtering methods. In reality, user-item interaction usually stems from more complex underlying factors, such as the users' specific preferences. A user-item bipartite graph could be used to understand the differences in motivation. However, existing research has not clearly proposed and modeled the factors that affect the differences, ignoring the similarities between user pairs and item pairs, preventing them from capturing fine-grained user preferences more effectively. In addition to the two points mentioned above, most GNN-based models for recommendation have the following two limitations: First, the model's accuracy depends on the number of observed interactions in the dataset. Secondly, node representations are vulnerable to noisy interactions. This work has developed a novel recommendation model called "Multi-Attribute and Implicit Relationship Factors With Self-Supervised Learning for Collaborative filtering" (MIS-CF), which explicitly proposes and models multi-attribute and implicit relationship factors for collaborative filtering recommendation. Meanwhile, an auxiliary self-supervised learning task is designed to help the downstream task optimize the node representation. MIS-CF aggregates multi-attribute spaces through the user-item bipartite graph and establishes user-user and item-item graphs to model the similar relationship information of neighbor pairs through a memory model. The self-supervised learning task generates contrastive learning via self-discrimination, thus mining the rich auxiliary signals within the data, improving the accuracy and robustness of our model. Moreover, the sparse regularizer is utilized to alleviate the overfitting problem. Extensive experimental results on three public datasets not only show the significant performance and robustness gain of the proposed model but also prove the effectiveness and interpretability of fine-grained implicit factors modeling. [ABSTRACT FROM AUTHOR]

In this research, we combine relational learning with multi-domain to develop a formal framework for a recommendation system. The design of our framework aims at: (i) constructing general rules for recommendations, (ii) providing suggested items with clear and understandable explanations, (iii) delivering a broad range of recommendations including novel and unexpected items. We use relational learning to find all possible relations, including novel relations, and to form the general rules for recommendations. Each rule is represented in relational logic, a formal language, associating with probability. The rules are used to suggest the items, in any domain, to the user whose preferences or other properties satisfy the conditions of the rule. The information described by the rule serves as an explanation for the suggested item. It states clearly why the items are chosen for the users. The explanation is in if-then logical format which is unambiguous, less redundant and more concise compared to a natural language used in other explanation recommendation systems. The explanation itself can help persuade the user to try out the suggested items, and the associated probability can drive the user to make a decision easier and faster with more confidence. Incorporating information or knowledge from multiple domains allows us to broaden our search space and provides us with more opportunities to discover items which are previously unseen or surprised to a user resulting in a wide range of recommendations. The experiment results show that our proposed algorithm is very promising. Although the quality of recommendations provided by our framework is moderate, our framework does produce interesting recommendations not found in the primitive single-domain based system and with simple and understandable explanations. [ABSTRACT FROM AUTHOR]

Trustworthy computing has recently attracted significant interest from researchers in several fields including multiagent systems, social network analysis, and recommender systems. As an additional dimension of information to past rating history, trust has been shown to be helpful for improving the accuracy of recommendations. Studies on the relationship between trust and rating behaviors may provide insights into the formation of trust in the context of online community, and lead to possible indicators for the effective use of trust in recommendations. In this paper, we study people's trust and rating behavior with the Epinions dataset. Epinions.com is a popular product review website allowing users to rate various categories of products, and establish a list of trustworthy users. We perform correlation analysis of activeness and trustworthiness defined by the number of ratings and the number of trustors to derive findings that can help the design of new decision support mechanisms in trust-based recommender systems. We then propose a trustee-influence based trust model where a trustee's activeness or trustworthiness is used to determine trust relationships. This trust model is incorporated into a memory-based and matrix factorization recommender systems to support online purchasing decision-making. Experimental results demonstrate the effectiveness of the proposed trust model for recommendation. [ABSTRACT FROM AUTHOR]

Recommender systems are changing from novelties used by a few E-commerce sites to serious business tools. They recommend products to customers based on their historical preferences. Through several recommendation techniques, Collaborative filtering (CF) is the most successful recommendation method which is widely used. Nowadays, customer lifetime value (CLV) is measured by RFM (Recency, Frequency, and Monetary) and weighted RFM-based method is used in product recommendation. In this paper, we present a product recommendation technique for online retail stores which employs CLV concept and integrates it with CF method to generate better quality recommendations. In this paper, CF is applied to customer ratings on products, which are collected implicitly by web usage mining approach. Product taxonomy is also used to segment products according to their categories and to reduce dimensions of computational space. The experimental results show that the proposed technique outperforms several other similar recommendation methods. [ABSTRACT FROM AUTHOR]