Social tagging has revolutionized the social and personal experience of users across numerous web platforms by enabling the organizing, managing, sharing and searching of web data. The extensive amount of information generated by tagging systems can be utilized for recommendation purposes. However, the unregulated creation of social tags by users can produce a great deal of noise and the tags can be unreliable; thus, exploiting them for recommendation is a nontrivial task. In this study, a new recommender system is proposed based on the similarities between user and item profiles. The approach applied is to generate user and item profiles by discovering tag patterns that are frequently generated by users. These tag patterns are categorized into irrelevant patterns and relevant patterns which represent diverse user preferences in terms of likes and dislikes. Furthermore, presented here is a method for translating these tag-based profiles into semantic profiles by determining the underlying meaning(s) of the tags, and mapping them to semantic entities belonging to external knowledge bases. To alleviate the cold start and overspecialization problems, semantic profiles are enriched in two phases: (a) using a semantic spreading mechanism and then (b) inheriting the preferences of similar users. Experiment indicates that this approach not only provides a better representation of user interests, but also achieves a better recommendation result when compared with existing methods. The performance of the proposed recommendation method is investigated in the face of the cold start problem, the results of which confirm that it can indeed remedy the problem for early adopters, hence improving overall recommendation quality.

With the rapid increasing rate of the high volume of social web contents due to the growing popularity of social media services, significant attention has been drawn towards recommender systems i.e. systems, that offer recommendations to users on items appropriate to their requirements. To offer suitable recommendations, the systems need comprehensive user and item models that would be able to provide thorough understanding of their characteristics and preferences. 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, and to enrich each individual profile by a two-phase profile enrichment procedure. The profiles are extended by association rules discovered through the association rule mining process. The user/item profiles are further enriched through collaboration with other similar user/item profiles. To evaluate the performance of this proposed approach, a real dataset from The Del.icio.us website is used for empirical experiment. Experimental result s 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.

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