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.
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