基于用户查询日志的查询聚类

贾荣飞; 金茂忠; 王晓博

基于用户查询日志的查询聚类

北京航空航天大学计算机学院, 北京 100191

基金项目: 国家863计划资助项目(2007AA010302); 国家自然科学基金资助项目(60603039,90718018)

详细信息

作者简介:
贾荣飞(1981-),男, 辽宁沈阳人,博士生,cjrf@sei.buaa.edu.cn.

中图分类号: TP 311
计量
- 文章访问数: 3656
- HTML全文浏览量: 179
- PDF下载量: 3045
- 被引次数: 0
出版历程
- 收稿日期: 2009-07-10
- 网络出版日期: 2010-04-30

Query clustering using user-query logs

School of Computer Science and Technology, Beijing University of Aeronautics and Astronautics, Beijing 100191, China

摘要

摘要: 基于用户查询日志提出了新的查询聚类算法.用户查询日志数据量大,比通常用于查询聚类的查询展现日志和查询点击日志更加稠密,不易产生聚类小的问题,但噪声多,不容易处理.为发现相似查询并减少噪声影响,同一用户同一时段的多次查询(共现查询)之间认为具有较高相似概率.在这一假设基础上,利用查询共现关系建立查询的邻居查询向量空间.将查询用邻居查询向量表示,邻居查询向量的相似度作为聚类中的查询相似度.应用改进的基于密度聚类算法完成聚类.实验证明,95262个查询组成数据集上,聚类算法实现查准率79.77%、查全率48.21%,平均聚类大小达到51.
- 聚类算法 /
- 搜索引擎 /
- 日志挖掘
Abstract: A new query clustering method on user-query log was presented. Traditional clustering techniques focused on queries and click-through logs, which are often sparse. The average cluster size is often small. In contrast, the user-query log is much denser as well as noisier. To reduce the influence of the noises and discover similar queries, queries visited by the same user at the same session were assumed to be mostly similar. Based on the assumption, a new similarity measure using query co-occurrence relations was calculated to create query neighbor vector space. The queries were represented by vectors consisting of their neighbors. The similarity function for clustering was calculated based on the query neighbor vectors. An adjusted clustering method of density-based spatial clustering of applications with noise(DBSCAN) was applied to generate the clusters. Experiments on a real dataset of 95262 queries show that 79.77% precision and 48.21% recall is achieved and the average cluster size achieves 51.
- clustering algorithms /
- search engines /
- data mining

HTML全文

参考文献(1)

[1] Wen Jirong,Nie Jianyun,Zhang Hongjiang.Query clustering using user logs[J].ACM Transactions on Information Systems,2002,20(1):59-81 [2] Fonseca B M,Golgher P B,De Moura E S,et al.Using association rules to discovery search engines related queries //1st Latin American Web Congress.Santiago:Citeseer,2003:66-71 [3] Beeferman D,Berger A L.Agglomerative clustering of a search engine query log //Proceedings of the 6th ACM SIGKDD International conference on Knowledge discovery and data mining.New York:ACM Press,2000:407-416 [4] Baeza-Yates R A,Tiberi A.Extracting semantic relations from query logs //Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.New York:ACM Press,2007:76-85 [5] Chan W,Leung W,Lee D.Clustering search engine query log containing noisy clickthroughs //Proceedings of SAINT Conference 2004.Tokyo:IEEE Computer Society,2004:305-308 [6] 张辉,谢科,庞斌,等.一种基于关键特征的搜索引擎结果聚类算法[J].北京航空航天大学学报,2007,33(6):739-742 Zhang Hui,Xie Ke,Pang Bin,et al.Key-feature-based clustering algorithm for search engine results[J].Journal of Beijing University of Aeronautics and Astronautics,2007,33(6):739-742(in Chinese)  [7] 张刚,刘悦,郭嘉丰,等.一种层次化的检索结果聚类方法[J].计算机研究与发展,2008,45(3):542-547 Zhang Gang,Liu Yue,Guo Jiafeng,et al.A hierarchical search result clustering method[J].Journal of Computer Research and Development,2008,45(3):542-547(in Chinese)  [8] Yi J,Maghoul F.Query clustering using click-through graph //Proceedings of the 18th International Conference on World Wide Web.Madrid:ACM Press,2009:1055-1056  [9] Deshpande M,Karypis G.Item-based top-n recommendation algorithms[J].ACM Transactions on Information Systems,2004,22(1):143-177 [10] Ester M,Kriegel H P,Sander J,et al.A density-based algorithm for discovering clusters in large spatial databases with noise //Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining.Portland:AAAI Press,1996:226-231

施引文献

资源附件(0)

访问统计