Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.
Digital Object Identifier (DOI) : 10.14569/IJARAI.2014.030106
Article Published in International Journal of Advanced Research in Artificial Intelligence(IJARAI), Volume 3 Issue 1, 2014.
Abstract: Trust-based recommender systems provide the recommendations on the most suitable items for the individual users by using the trust values from their trusted friends. Usually, the trust values are obtained directly from the users, or by calculated using the similarity values between the pair of users. However, the current trust value evaluation can cause the following three problems. First, it is difficult to identify the co-rated items for calculating the similarity values between the users. Second, the current trust value evaluation still has symmetry property which makes the same trust value on both directions (trustor and trustee). Finally, the current trust value evaluation does not focus on how to adjust the trust values for the remote user. To eliminate all of these problems, our purposed method consists of three new factors. First, the similarity values between the users are calculated using a latent factor model instead of the co-rated items. Second, in order to identify the trustworthiness for every user in trust network, the degrees of reliability are calculated. Finally, we use the number of hops for adjusting the trust value for the remote users who are expected to be low trust as shown in the real-world application concept. This trust evaluation leads to better predicted rating and getting more predictable ratings. Consequently, from our experiment, the more efficiency trust-based recommender system is obtained, comparing with the classical method on both accuracy and coverage.
Sajjawat Charoenrien and Saranya Maneeroj, “A New Trust Evaluation for Trust-based RS” International Journal of Advanced Research in Artificial Intelligence(IJARAI), 3(1), 2014. http://dx.doi.org/10.14569/IJARAI.2014.030106