ANTI-FOLKSONOMICAL ITEM RECOMMENDATION SYSTEM BASED ON SIMILARITIES BETWEEN ITEM CLUSTERS IN SOCIAL BOOKMARKING

摘要

Web-based bookmark management services called social book marking has been in the spotlight recently. Social Book marking allows users to add several keywords called tags to items they bookmarked. Many previous works on social book marking using actual words for tags, called folksonomy, have come out. However, essential information of tags is not represented in their tag names, but in the classification of items by tags. Based on this assumption, we propose an anti-folksonomical recommendation system for calculating similarities between groups of items classified according to tags. In addition, we use hypothesis testing to improve these similarities based on statistical reliability. The experimental results show that our proposed system provides an appropriate recommendation result even if users tagged with different keywords.