Personal classification space-based collaborative filtering algorithms

Abstract

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.