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 f...

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Bibliographic Details
Published in:Computers & electrical engineering 2018-02, Vol.66, p.174-188
Main Authors: Lingam, Greeshma, Rout, Rashmi Ranjan, Somayajulu, DVLN
Format: Article
Language:English
Subjects:
Algorithms
Automation
Big Data
Computing time
Decision making
Digital media
Entropy (Information theory)
Experimentation
Learning automata
Machine learning
Online social networks
Recommended trust path
Recommender systems
Response time (computers)
Social networks
Trust
Trustworthiness
Websites
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Summary: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.
ISSN:0045-7906
1879-0755
DOI:10.1016/j.compeleceng.2017.10.017