一种流行性与相似性结合查询推荐策略  

A Popularity Versus Similarity Query Recommendation Strategy

作　　者：孙达明[1] 张斌[1] 张书波[1] 马安香[1] 

机构地区：[1]东北大学信息科学与工程学院,沈阳110819

出　　处：《小型微型计算机系统》2016年第6期1121-1125,共5页Journal of Chinese Computer Systems

基　　金：中央高校东北大学基本科研专项基金项目(N120804001;N120204003)资助;宁夏回族自治区自然科学基金项目(NZ13265)资助;国家自然科学基金项目(61272182)资助

摘　　要：基于日志的搜索引擎查询推荐方法大多从搜索行为流行性考虑进行查询推荐,这类推荐方法忽略了个体用户背景不同带来的搜索需求的差异,对于一些在搜索引擎日志中有较多记录的用户来讲,难以满足他们更高质量的搜索需求.然而,此类用户的搜索记录又不足以支持系统提供个性化的推荐服务.所以,针对在搜索引擎日志中有较多记录的用户,本文提出一种折衷的解决方案,即流行性与相似性结合的查询推荐策略,提供一种近似于个性化的推荐服务.本文采用社团发现的方法发现搜索日志中的密集行为,从而构建典型用户行为模型代表用户搜索背景,并引入模块度度量典型用户行为的强度,最后结合流行性、群体用户对推荐词的支持程度、用户对用户群体的归属程度以及典型用户行为的强度来产生推荐词集合.本文最后,在真实数据集的实验结果证明了本策略能够在控制推荐失败风险的同时提高用户对推荐结果的满意度.Most query recommendations based on the search log are with the aspect of popularity. These kinds of query recommenda- tions methods ignore the difference in search demand caused by different backgrounds of users. Sometimes, the query recommendations method based on popularity could not provide high quality service even the users have a lot of search record in the log. So, a balanced solution is proposed in this paper, serving as a search recommendation strategy which is popularity versus similarity providing recom- mendation service near to personalized. Intensive behavior block based on query-flow graph are discovered from search records based on community structure detection to construct representative user behavior model which describes the search background of users and introduce modularity which measures the strength of user behavior. The popularity, the degree of support, the degree of membership and the strength of user behavior are proposed to produce the result of query recommendations. Extensively experiments are conducted on a real query log, and the results show that this method can reduce the risk of recommendation failure and improve the satisfaction of users for recommendation.

关 键 词：计算机应用 搜索引擎 查询推荐 查询流图 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]