Trust aware recommender system using swarm intelligence

Due to limitations and challenges faced by traditional collaborative filtering-based recommender systems, researchers have been shifting their attention towards using trust information among users while generating recommendations. It is observed that one trust metric may work better for some user and fails to do so in the case of another user. This paper proposes to favor that metric which provides high-quality recommendations for a particular user. For this purpose, weights have been assigned to various trust metrics for each pair of users and optimized iteratively to generate more accurate and personalized recommendations. We have used swarm intelligent techniques namely Bat algorithm and Particle Swarm Optimization for the same. The performance of the approach proposed in this work is evaluated using MovieLens, Epinions, CiaoDVD, and Filmtrust data sets and compared to earlier works generating recommendations using an individual metric. The results indicate that the MAE while using a combination of weighted trust is 0.59 (with PSO) and 0.55 (with Bat), which is much better compared to using a single metric. Bat also generates better recommendations with an accuracy of 85.45% than PSO (81.85%). Also, the MAE found using Bat was 3.84% better when utilizing denser datasets (MovieLens and FilmTrust) as compared to sparse datasets (CiaoDVD and Epinions).