Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Tag-aware recommender systems by fusion of collaborative filtering algorithms
Proceedings of the 2008 ACM symposium on Applied computing
Recommending scientific articles using citeulike
Proceedings of the 2008 ACM conference on Recommender systems
Tag-based filtering for personalized bookmark recommendations
Proceedings of the 17th ACM conference on Information and knowledge management
Collaborative filtering for social tagging systems: an experiment with CiteULike
Proceedings of the third ACM conference on Recommender systems
Extending a hybrid tag-based recommender system with personalization
Proceedings of the 2010 ACM Symposium on Applied Computing
Content-based recommendation in social tagging systems
Proceedings of the fourth ACM conference on Recommender systems
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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 book marking 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 book marking 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.