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Hill Climbing for Diversity Retrieval

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2 Author(s)
Chein-Shung Hwang ; Dept. of Inf. Manage., Chinese Culture Univ., Taipei, Taiwan ; Show-Fen Lin

Case-based recommender systems have been widely applied in suggesting products that are most similar to current user's query. By prioritizing similarity during a case-based approach may degrade the quality of the retrieval results. There have been a number of attempts to increase retrieval diversity. However, there is a trade-off between similarity and diversity. The improvements in diversity may lead to the loss of similarity. In this paper, we propose a new retrieval strategy based on the random-restart hill-climbing algorithm which optimizes the trade-off between similarity and diversity. Experimental results show that the proposed algorithm can achieve a better overall quality than other approaches.

Published in:

Computer Science and Information Engineering, 2009 WRI World Congress on  (Volume:5 )

Date of Conference:

March 31 2009-April 2 2009