Trust Modeling in Recommendation
Mehmet Utku Demirci, Pınar Karagöz · 2021
In social networks, trust is a fundamental notion affecting the nature and the strength of ties between individuals. It is also a piece of useful auxiliary information for improving the performance of recommendation systems. The number of ratings given by a user is minimal compared to all items in popular, widely-used e-commerce sites. Therefore, the user-item matrix that is used in collaborative filtering suffers from data sparsity, resulting in poor recommendation quality. Another issue is the cold start problem, which occurs for the inclusion of new users and new items to the system. Trust notion is helpful for alleviating the effect of these problems by providing additional relationships between the users and pointing out strong relationships. Information as to the trust between users can be explicitly available. However, such information is not widely available, and hence implicit trust models have been employed. This work analyzes two sub-problems under trust modeling for recommendation: (1) What is the relationship between explicit and implicit trust scores, are they replaceable? (2) Can we model explicit trust in a trust network? For the first problem, we present an implicit trust model and analyze the compatibility of implicit and explicit trust scores. For the second problem, we model explicit trust modeling as a link prediction problem and analyze the performance of the prediction models we generate on a set of benchmark data sets.