‎Provenance Based Trust Boosted Recommender System Using Boosted Vector Similarity Measure

Subject Areas : Transactions on Fuzzy Sets and Systems

Dhanalakshmi Teekaraman ¹ , Sendhilkumar Selvaraju ² , Mahalakshmi Guruvayur Suryanarayanan ³

1 - Department of Computer Science and Engineering, Associate Professor, Jerusalem College of Engineering, Chennai, India.
2 - Department of Information Science and Technology, Professor, Anna University, CEG Campus, Chennai, India.
3 - Department of Computer Science and Engineering, Associate Professor, Anna University, CEG Campus, Chennai, India.

Received: 2023-05-31 Accepted : 2023-10-08 Published : 2023-11-07

Keywords: Social network, Provenance, Trust, Fuzzy rule, Fuzzy vector space, Multi-attribute.,

Abstract :

As users in an online social network are overwhelmed by the abundant amount of information, it is very hard to retrieve the preferred or required content. In this context, an online recommender system helps to filter and recommend content such as people,items or services. But, in a real scenario, people rely more on recommendations from trusted sources than distrusting sources. Though, there are many trust based recommender systems that exist, it lag in prediction error. In order to improve the accuracy of the prediction, this paper proposes a Trust-Boosted Recommender System (TBRS). Since, the provenance derives the trust in a better way than other approaches, TBRS is built from the provenance concept. The proposed recommender system takes the provenance based fuzzy rules which were derived from the Fuzzy Decision Tree. TBRS then computes the multi-attribute vector similarity score and boosts the score with trust weight. This system is tested on the book-review dataset to recommend the top-k trustworthy reviewers.The performance of the proposed method is evaluated in terms of MAE and RMSE. The result shows that the error value of boosted similarity is lesser than without boost. The reduced error rates of the Jaccard, Dice and Cosine similarity measures are 18%, 15% and 7% respectively. Also, when the model is subjected to failure analysis, it gives better performance for unskewed data than slewed data. The models fbest, average and worst case predictions are 90%, 50% and <23% respectively.

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