Computer Science and Information Engineering, World Congress on (2009)
Los Angeles, California USA
Mar. 31, 2009 to Apr. 2, 2009
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CSIE.2009.624
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.
Case-Based Reasoning, Hill Climbing, Diversity Retrieval
C. Hwang and S. Lin, "Hill Climbing for Diversity Retrieval," 2009 WRI World Congress on Computer Science and Information Engineering, CSIE(CSIE), Los Angeles, CA, 2009, pp. 154-158.