A timeline-based algorithm for personalized tag recommendation

Authors:
Zhaohui Yu;Puwei Wang;Xiaoyong Du;Jianwei Cui;Tianren Xu
Affiliations:
Key Labs of Data Engineering and Knowledge Engineering Ministry of Education, China and School of Information, Renmin University of China, Beijing, China;Key Labs of Data Engineering and Knowledge Engineering Ministry of Education, China and School of Information, Renmin University of China, Beijing, China;Key Labs of Data Engineering and Knowledge Engineering Ministry of Education, China and School of Information, Renmin University of China, Beijing, China;Key Labs of Data Engineering and Knowledge Engineering Ministry of Education, China and School of Information, Renmin University of China, Beijing, China;Key Labs of Data Engineering and Knowledge Engineering Ministry of Education, China and School of Information, Renmin University of China, Beijing, China
Venue:
WISS'10 Proceedings of the 2010 international conference on Web information systems engineering
Year:
2010

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Abstract

Recently, tagging has been a flexible and important way to share and categorize web resources, these user-generated tags are effective to represent user interests because these tags reflect human being's judgments while more concise and closer to human understanding, and the user interests are changing over time. Thus, modeling user interests to meet individual user needs is an important challenge for personalization and information filtering applications, such as recommender systems. In this paper, we apply a distance decay model for modeling user interests in terms of tags based on timeline. We then propose a novel algorithm to measure users' similarities in terms of their tagging activity over a specific time period and provide personalized tag recommendation according to similar users' interests in their next time intervals. Experimental results demonstrate the higher precision and recall with our personalized tag recommendation algorithm than other existing methods.