基于日志挖掘的电商查询建议方法

摘要

查询建议可以有效减少用户输入、消除查询歧义,提高信息检索的便捷性和准确率.随着电子商务的发展,查询建议也越来越多地应用于电子商务网站的商品搜索中.然而,传统的基于Web搜索的查询建议方法在电商领域并不能完全适用.针对电商这一特定领域,对不同的查询建议技术进行比较,提出了一种综合考虑用户的搜索以及购物行为的查询建议方法,运用MapReduce技术对用户日志进行挖掘,以此生成检索词词库;并通过在线计算与离线计算结合的方法,为用户提供实时查询建议.实验结果表明,本文提出的基于日志挖掘的电商查询建议方法能有效提高查询建议的准确率,并且具有良好的处理性能.%Query suggestion can effectively alleviate the input burden for users,eliminate the query ambiguity,and improve theconvenience and accuracy of information retrieval.With the development of e-commerce,query suggestion is also popular in the product search of e-commerce applications.However,traditional query suggestion methods for Web search are not fully applicable in e-commerce applications.Based on the analysis of different query suggestion techniques,an e-commerce query suggestion method based on log mining is presented,which considersboth the search behaviors and shopping behaviors of users.MapReduce is used in log mining to generate the query words in an offline mode,and query suggestions are offeredto users in an online mode.Experimental results show that the presented method can improves the accuracy of querysuggestions and has good performance.