Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6883481 | Computers & Electrical Engineering | 2018 | 15 Pages |
Abstract
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Networks and Communications
Authors
Greeshma Lingam, Rashmi Ranjan Rout, DVLN Somayajulu,