Learning automata-based trust model for user recommendations in online social networks

Nowadays, most of the online social media websites provide recommendations as service for selective decision making. Determining a recommended trust path based on the consumer's non-functional requirements, such as availability of the products, delay for computing recommendations and response time for a good recommendation is one of the challenging issues in online social networks. In this paper, we first design a recommendation-based online social network architecture by incorporating trust information (namely, direct trust and indirect trust), relevance degree and recommended influence value. We propose a high quality of social trust associated model for evaluating a recommended trust path. The proposed model estimates utility values with associated weights based on Shannon entropy information gain. Further, for best recommended trust path selection, we propose a Learning Automata based Recommended Trust Path Selection (LA-RTPS) algorithm to identify multiple recommended trust paths and to determine an aggregate path. The experimentation using real time datasets illustrates the efficacy of the proposed algorithm.