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
Chein-Shung Hwang, Show-Fen Lin, "Hill Climbing for Diversity Retrieval", CSIE, 2009, Computer Science and Information Engineering, World Congress on, Computer Science and Information Engineering, World Congress on 2009, pp. 154-158, doi:10.1109/CSIE.2009.624