Tag Based Recommender System for Social Bookmarking Sites

It is often essential for people to consult with others and ask them about their past experience and thoughts when making choices. Exchanging ideas among people has become more meaningful since the extensive growth of information on the World Wide Web (WWW). People have access to tremendous amount of information, but choosing the most relevant information is of high effort. It was when recommender systems came into existence in 1992 in order to assist users in the process of finding the most appropriate information on WWW, and identify sets of items which are likely to be interesting for the users. Recommender systems have used different sources of data in order to identify users' interests. In addition, by growth of social resource sharing like social bookmarking sites, tagging activities can be considered as explicit knowledge for user and item modeling. Existing recommender systems lack use of external source of information for recommending the most appropriate item. They mainly use the information of their own website, while there is valuable information on the web which could improve the performance of the predictions. In this paper, we use Open Directory Project (ODP) data as external knowledge about web pages in addition to tagging activities of users in a social bookmarking site. We have designed a content based recommender system which can recommend the most relevant web pages for each user based on the user's profile and gathered information about web pages from ODP as implicit data. We empirically evaluate effect of ODP data on the predictions using Delicious dataset in order to analyze the performance of the proposed method. The results show that our recommender system outperforms when it uses ODP information as external source of data.