Finding related micro-blogs based on wordnet

Authors:
Lin Li;Huifan Xiao;Guandong Xu
Affiliations:
School of Computer Science & Technology, Wuhan University of Technology, Wuhan, China;School of Computer Science & Technology, Wuhan University of Technology, Wuhan, China;Centre for Applied Informatics, Victoria University, Victoria, Australia
Venue:
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications
Year:
2012

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Abstract

In the common formulation, the recommendation problem is reduced to the problem of estimating the utilization for the items that have not been seen by a user [1]. Micro-blog recommendation will recommend micro-blogs interest users, mostly those related to the micro-blogs that a user had issued or trending topics. One indispensable step in realizing effective recommendation is to compute short text similarities between micro-blogs. In this paper, we utilize two kinds of approaches, traditional cosine-based approach and WordNet-based semantic approach, to compute similarities between micro-blogs and recommend top related ones to users. We conduct experimental study on the effectiveness of two approaches using a set of evaluation measures. The results show that semantic similarity based approach has relatively higher precision than that of traditional cosine-based method using 548 twitters as dataset.