Re-ranking Search Results Using Semantic Similarity

摘要：

In this paper, we propose a re-ranking method which employs semantic similarity to improve the quality of search results. We fetch the top N results returned by search engine, and use semantic similarities between the candidate and the query to re-rank the results. We first convert the ranking position to an importance score for each candidate. Then we combine the semantic similarity score with this initial importance score and finally we get the new ranks. In the experiment, we use NDCG to evaluate the reranking results and the experimental results validate that our proposed method can indeed improve the search performance and meet users need to a certain extent.

作者: Ruofan Wang Shan Jiang Yan Zhang Min Wang

作者单位: Department of Machine Intelligence, Peking University, Beijing 100871, China Key Laboratory on Machine Perception, Ministry of Education, Beijing 100871, China Ucap Corporation, DongGuan, Guangdong, China

会议类型: 国际会议

会议名称: 2011 Eighth International Conference on Fuzzy System and Knowledge Discovery(第八届模糊系统与知识发现国际会议 FSKD 2011)

会议地点: 上海

会议语种:英文

页码: 1092-1096

在线出版日期: 2011-07-26（万方平台首次上网日期，不代表论文的发表时间）