Affiliations: [a] Computer Science and Engineering, Lovely Professional University, Punjab, India | [b] Computer Science and Informatics, Central University, Himachal Pradesh, India | [c] BBK DAV College, Amritsar, India
Correspondence:
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Corresponding author: Munish Bhatia, Computer Science and Engineering, Lovely Professional University, Punjab, India. E-mail: [email protected].
Abstract: Recommender System has become one of the most effective tools for provisioning user-interest based decision-making services. With its capability to generate efficient recommendations, users are directed towards items that are optimal with compliance to their needs, and preferences. Inspired from these aspects, this paper presents a novel recommendation technique based on context-specific information and social network analysis for determining dependable items. Context specific information provides a quantifiable measure of user interest for dependability whereas social network analysis determines the degree of similarity among other users. Both types of information are acquired and analyzed in the form of linguistic terms. This fuzzy-based quantification provides an effective way to evaluate social-ratings and social-similarity. For validation, it is evaluated in the on-line mobile purchase scenario. Based on the numerous simulations performed on different data sets, performance estimators in the form of Temporal Delay, Statistical Analysis and System Stability are estimated. It is concluded that the proposed mechanism of recommendation is effective and efficient in comparison to state-of-the-art recommender systems.
Keywords: Recommender system, social network analysis, context-specific information, item-dependability, fuzzy number set
