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Learning automata-based trust model for user recommendations in online social networks.
- Source :
-
Computers & Electrical Engineering . Feb2018, Vol. 66, p174-188. 15p. - Publication Year :
- 2018
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00457906
- Volume :
- 66
- Database :
- Academic Search Index
- Journal :
- Computers & Electrical Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 128517927
- Full Text :
- https://doi.org/10.1016/j.compeleceng.2017.10.017