An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Web Page Recommender System based on Folksonomy Mining for ITNG '06 Submissions
ITNG '06 Proceedings of the Third International Conference on Information Technology: New Generations
Tag-aware recommender systems by fusion of collaborative filtering algorithms
Proceedings of the 2008 ACM symposium on Applied computing
Using Ontology to Enhance Collaborative Recommendation Based on Community
WAIM '08 Proceedings of the 2008 The Ninth International Conference on Web-Age Information Management
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Development of an adaptive and intelligent tutoring system by expert system
International Journal of Computer Applications in Technology
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This research proposes a new similar information recommendation system focusing on social bookmarking, which organises bookmarks by using tags. Since social bookmarking targets a wide variety of genres of web pages, this research handles the problem where the standard collaborative filtering method cannot offer recommendations with a better level of precision. This paper improves the collaborative filtering algorithm for users of social bookmark services. A user's bookmarks are placed on his/her own classifying space made of tags. These bookmarks are transformed into a degree of similarity for recommendations. The degree is used to compare the personal classifying space with another's space. Comparison with previous studies confirms the superiority of the method based on space classification, in particular, where the cos distance with the distribution weight added is used as similarity between items. This proposed method shows a significant superiority.