Time and ontology for resource recommendation system

Abstract Nowadays social tagging systems have considerable growth. These systems help users to find their favorite resources among the large volume of information. Many ways have been proposed for recommending. Since the tags are connotations of users’ interests and also the time of assigning tags indicates the current interests of users, so the combination of semantic influence of tags and time of tag assignment information can effect on the accuracy of recommendations. In this paper, an item recommendation system is proposed that by using important available information in tagging systems, e.g. time, and also using available ontology, the accuracy of recommended results has been improved. The evaluation of the proposed system is performed on movielens.org dataset. The results in comparing with other methods demonstrated the improving quality of the proposed system.

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