Leveraging Overlapping Communities Detection Improve Personalized Recommendation in Folksonomy Networks
SU Xiao-pin · Journal of Chinese Computer Systems · 2013
In folksonomy based networks,users are allowed to annotate resources with personalized tags,which can facilitate users conveying the user's interest and preference information.However,this flexibility and loosening method of classification brings with it certain costs: redundant,ambiguous and polysemy,which can render resource discovery difficult.So,in tag-based recommendation,the recommended result of precision and diversity is low and has a poor user experience.Communities detection(clustering) provides a means to remedy these problems.Starting from a tagging co-occurrence network,we leverage overlapping communities detection method in tagging co-occurrence network to comprehend the proper meaning of the tags and reduce tagging noise.Based on overlapping communities detection,a complete scheme of personalized recommendation was presented.We validate this approach through evaluation of proposed personalization algorithm using data from a real collaborative tagging Web site,the result demonstrates that overlapping communities detection could considerably improve the precision and diversity of recommendations.