Trust and reputation-based multi-agent recommender system Online publication date: Mon, 06-Aug-2018
by Punam Bedi; Sumit Kumar Agarwal; Richa
International Journal of Computational Science and Engineering (IJCSE), Vol. 16, No. 4, 2018
Abstract: User profile modelling for the domain of tourism is different compared with most of the other domains, such as books or movies. The structure of a tourist product is more complex than a movie or a book. Moreover, the frequency of activities and ratings in tourism domain is also smaller than the other domains. To address these challenges, this study proposes a trust and reputation-based collaborative filtering (TRbCF) algorithm. It augments a notion of dynamic trust between users and reputation of items to existing collaborative approach for generating relevant recommendations. A multi-agent recommender system for e-tourism (MARST) for recommending tourism services using TRbCF algorithm is designed and a prototype is developed. TRbCF also helps to handle new user cold-start problem. The developed system is capable to generate recommendations for hotels, places to visit and restaurants at a single place whereas most of the existing recommender systems focus on one service at a time.
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