{"id":351830,"date":"2019-01-17T09:47:46","date_gmt":"2019-01-17T17:47:46","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=351830"},"modified":"2019-01-17T09:47:46","modified_gmt":"2019-01-17T17:47:46","slug":"study-topic-similarity-measures","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/study-topic-similarity-measures\/","title":{"rendered":"A Study of Topic Similarity Measures"},"content":{"rendered":"

In this poster we describe an investigation of topic similarity measures. We elicit assessments on the similarity of 10 pairs of topic from 76 subjects and use these as a benchmark to assess how well each measure performs. The measures have the potential to form the basis of a predictive technique, for adaptive search systems. The results of our evaluation show that measures based on the level of correlation between topics concords most with general subject perceptions of search topic similarity.<\/p>\n","protected":false},"excerpt":{"rendered":"

In this poster we describe an investigation of topic similarity measures. We elicit assessments on the similarity of 10 pairs of topic from 76 subjects and use these as a benchmark to assess how well each measure performs. The measures have the potential to form the basis of a predictive technique, for adaptive search systems. 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