Mining subtopics from different aspects for diversifying search results

  • Authors:

  • Chieh-Jen Wang;Yung-Wei Lin;Ming-Feng Tsai;Hsin-Hsi Chen

  • Affiliations:

  • Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan 10617;Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan 10617;Department of Computer Science and Program in Digital Content and Technologies, National Chengchi University, Taipei, Taiwan 11605;Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan 10617

  • Venue:
  • Information Retrieval
  • Year:
  • 2013

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Abstract

User queries to the Web tend to have more than one interpretation due to their ambiguity and other characteristics. How to diversify the ranking results to meet users' various potential information needs has attracted considerable attention recently. This paper is aimed at mining the subtopics of a query either indirectly from the returned results of retrieval systems or directly from the query itself to diversify the search results. For the indirect subtopic mining approach, clustering the retrieval results and summarizing the content of clusters is investigated. In addition, labeling topic categories and concept tags on each returned document is explored. For the direct subtopic mining approach, several external resources, such as Wikipedia, Open Directory Project, search query logs, and the related search services of search engines, are consulted. Furthermore, we propose a diversified retrieval model to rank documents with respect to the mined subtopics for balancing relevance and diversity. Experiments are conducted on the ClueWeb09 dataset with the topics of the TREC09 and TREC10 Web Track diversity tasks. Experimental results show that the proposed subtopic-based diversification algorithm significantly outperforms the state-of-the-art models in the TREC09 and TREC10 Web Track diversity tasks. The best performance our proposed algorithm achieves is 驴-nDCG@5 0.307, IA-P@5 0.121, and 驴#-nDCG@5 0.214 on the TREC09, as well as 驴-nDCG@10 0.421, IA-P@10 0.201, and 驴#-nDCG@10 0.311 on the TREC10. The results conclude that the subtopic mining technique with the up-to-date users' search query logs is the most effective way to generate the subtopics of a query, and the proposed subtopic-based diversification algorithm can select the documents covering various subtopics.