Abstract
Social tagging provides an effective way for users to organize, manage, share and search for various kinds of resources. These tagging systems have resulted in more and more users providing an increasing amount of information about themselves that could be exploited for recommendation purposes. However, since social tags are generated by users in an uncontrolled way, they can be noisy and unreliable and thus exploiting them for recommendation is a non-trivial task.
In this article, a new recommender system is proposed based on the similarities between user and item profiles. The approach here is to generate user and item profiles by discovering frequent user-generated tag patterns. We present a method for finding the underlying meanings (concepts) of the tags, mapping them to semantic entities belonging to external knowledge bases, namely WordNet and Wikipedia, through the exploitation of ontologies created within the W3C Linking Open Data initiative. In this way, the tag-base profiles are upgraded to semantic profiles by replacing tags with the corresponding ontology concepts. In addition, we further improve the semantic profiles through enriching them with a semantic spreading mechanism.
To evaluate the performance of this proposed approach, a real dataset from The Del.icio.us website is used for empirical experiment. Experimental results demonstrate that the proposed approach provides a better representation of user interests and achieves better recommendation results in terms of precision and ranking accuracy as compared to existing methods. We further investigate the recommendation performance of the proposed approach in face of the cold start problem and the result confirms that the proposed approach can indeed be a remedy for the problem of cold start users and hence improving the quality of recommendations.