Abstract
Recommender systems currently used in many applications, including tourism, tend to simply be reactive to user request. The recommender system proposed in this paper uses multi-agents and multi-dimensional contextual information to achieve proactive behavior. User profile and behavior get implicitly incorporated and subsequently updated in the system. The recommender system has been developed and applied to the tourism domain. It was tested and evaluated by relatively large set of real users The evaluation conducted shows that most of the users are satisfied with the functionality of the system and its ability to produce the recommendation adaptively and proactively taking into considerations different factors.
Highlights
Recommender systems are normally used to filter huge amounts of information in order to provide users with recommendations pertaining to products/services
Beside the attempts the researchers have done to apply MD and use the contextual information using a hybrid recommender system, we have decided to enhance this approach of MD recommender system [6]
In this paper we propose a multi-agent recommender system that is based on multi-dimensional rating approach using the Knowledge-base Hybrid recommender system
Summary
Recommender systems are normally used to filter huge amounts of information in order to provide users with recommendations pertaining to products/services. Beside the attempts the researchers have done to apply MD and use the contextual information using a hybrid recommender system, we have decided to enhance this approach of MD recommender system [6]. This MD recommender system [6] was tested based on movies datasets from which the traditional two-dimensional CF approach was applied. Reduction based theory has been used in order to reduce the multi dimensions to two dimensions allowing to use contextual information as additional dimensions
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