采用查询建议技术表现用户查询意图的多样化，并自动向用户提供多种选择，是当前搜索引擎普遍的做法。但当前的查询建议研究鲜有考虑时间因素对生成查询建议的影响，而实际上在很多情况下，用户的查询意图会随着时间的推移发生改变。为此，根据时间点击图挖掘原理提出一种查询建议方法。对原始的查询日志文件进行预处理，生成时间点击图。对时间点击图进行非连通子图检测和图的合并操作，以降低或消除图的非连通性。采用基于随机游走模型的图挖掘算法，生成给定查询的查询建议集。在真实的Web环境下进行实验，结果表明，利用该方法能够提高查询建议的精度和差异度，从而生成更加可靠的查询建议。%A common practice for search engines is that exploiting query suggestion demonstrates the diversity of user’ s query intent and automatically provides alternatives to them. For current researches on query suggestion,however,there are few studies that focus on the influence of time on formulation of query suggestions. Actually,in many cases,the query intent of users can change over time. This paper presents a temporal click graph mining based method for query suggestion. A raw query log file is preprocessed,and a temporal click-through graph can be generated by it. To eliminate the temporal click-through graphs’ non-connectivity,two basic operations:checking and merging disconnected subgraphs will be executed over it. A random walk based graph mining algorithm is exploited to generate a set of query suggestions for a given query. It conducts the extensive experiment over real Web environment,and experimental results show that this approach aims at improving the precision and difference of query suggestions,thereby can generate more reliable query suggestions.
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