[1]ZHANG Sen,ZHANG Chen,LIN Peiguang,et al.Web search topic analysis based on user search query logs[J].CAAI Transactions on Intelligent Systems,2017,12(5):668-677.[doi:10.11992/tis.201706096]
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Web search topic analysis based on user search query logs

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 12 Number of periods: 2017 5 Page number: 668-677 Column: 学术论文—自然语言处理与理解 Public date: 2017-10-25
Title:
Web search topic analysis based on user search query logs
Author(s):
ZHANG Sen1;  ZHANG Chen1; 2;  LIN Peiguang1;  ZHANG Chunyun1;  GUO Yuchao1;  REN Weilong1;  REN Ke2
1. School of Computer Science & Technology, Shandong University of Finance & Economics, Jinan 250014, China;
2. Department of Computer Science & Engineering, Hong Kong University of Science and Technology, Hong Kong 999077, China
Keywords:
web search; search engine; natural language processing; topic model; data mining; burstiness; temporal analysis; parameter estimate
CLC:
TP391
DOI:
10.11992/tis.201706096
Abstract:
Web search analysis plays a critical role in improving the performance of contemporary search engines. In addition, search engine accuracy can be improved by analyzing the individual search properties of users. Most existing models, such as the click graph and its variants, focus on the common characteristics of the group. However, as yet, there has been little investigation of a model that would obtain both the collective group characteristics and the unique characteristics of individual users. In this paper, we investigate user-specific web search analysis, whereby we obtain the topic distributions of the search queries of individual users by determining the burstiness of user searches. We propose two topic models, i.e., the search burstiness model (SBM) and the coupling-sensitive search burstiness model (CS-SBM). The SBM adopts the assumption that the query words and URL are topically independent, The CS-SBM supposes that the query words and URL are topically relevant. The obtained topic distribution information is stored in skewed Dirichlet priors and a beta distribution is used to capture the temporal properties of the user searches. Our experimental results show that each user’s web search trail has unique characteristics, and that in the case of there being a large amount of real query log data, in comparison to the latent Dirichlet allocation (LDA) and topic over time (TOT) models, our proposed models have advantages with respect to generalized performance and effectively describes the temporal change process of user search queries.
References:
[1] SUNEHAG P. Using two-stage conditional word frequency models to model word burstiness and motivating TF-IDF[J]. Journal of machine learning reasearch, 2017, 2:8.
[2] ELKAN C. Clustering documents with an exponential-family approximation of the Dirichlet compound multinomial distribution[C]//Proceedings of the 23rd International Conference on Machine Learning, Pittsburgh. Pennsylvania, USA, 2006:289-296.
[3] DOYLE G, ELKAN C. Accounting for burstiness in topic models[C]//Proceedings of the 26th Annual International Conference on Machine Learning Montreal. QC, Canada, 2009:281-288.
[4] XUE G R, ZENG H J, CHEN Z, et al. Optimizing web search using web click-through data[C]//Proceedings of the thirteenth ACM international conference on Information and Knowledge Management. Washington, USA, 2004:118-126.
[5] GUO F, LIU C, WANG Y M. Efficient multiple-click models in web search[C]//Proceedings of the Second ACM International Conference on Web Search and Data Mining. Barcelona, Spain, 2009:124-131.
[6] 张宇, 宋巍, 刘挺, 等. 基于URL主题的查询分类方法[J]. 计算机研究与发展, 2012, 49(6):1298-1305. ZHANG Yu, SONG Wei, LIU Ting, et al. Query classification based on url topic[J]. Journal of computer research and development, 2012, 49(6):1298-1305.
[7] MADSEN R E, KAUCHAK D, ELKAN C. Modeling word burstiness using the dirichlet distribution[C]//Proceedings of the 22nd international conference on Machine iearning. Bonn, Germany, 2005:545-552.
[8] BLEI D M, NG A Y, JORDAN M I. Latent dirichlet allocation[J]. Journal of machine learning research, 2003, 3(1):993-1022.
[9] WANG X, MCCALLUM A. Topics over time:a non-Markov continuous-time model of topical trends[C]//Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining. Philadelphia, USA, 2006:424-433.
[10] 徐戈, 王厚峰. 自然语言处理中主题模型的发展[J]. 计算机学报, 2011, 34(8):1423-1436. XU Ge, WANG Houfeng. The development of topic model in natural language processing[J]. Chinese journal of computers, 2011, 34(8):1423-1436.
[11] 张晨逸, 孙建伶, 丁轶群. 基于MB-LDA模型的微博主题挖掘[J]. 计算机研究与发展, 2011, 48(10):1795-1802.ZHANG Chenyi, SUN Jianling, DING Yiqun. Topic mining for microblog based on mb-lda model[J]. Journal of computer research and development, 2011, 48(10):1795-1802.
[12] 刘少鹏, 印鉴, 欧阳佳, 等. 基于MB-HDP模型的微博主题挖掘[J]. 计算机学报, 2015, 38(7):1408-1419. LIU Shaopeng, YIN Jian, OUYANG Jia, et al. Topic mining from microblogs based on MB-HDP model[J]. Chinese Journal of Computers, 2015, 38(7):1408-1419.
[13] JIANG D, TONG Y, SONG Y. Cross-lingual topic discovery from multilingual search engine query log[J]. ACM transactions on information systems (TOIS), 2016, 35(2):9.
[14] JIANG D, LEUNG K W T, NG W. Query intent mining with multiple dimensions of web search data[J]. World wide web, 2016, 19(3):475.
[15] JIANG D, YANG L. Query intent inference via search engine log[J]. Knowledge and information systems, 2016, 49(2):661-685.
[16] HUANG J, EFTHIMIADIS E N. Analyzing and evaluating query reformulation strategies in web search logs[C]//Proceedings of the 18th ACM Conference on Information and Knowledge Management. Hong Kong, China, 2009:77-86.
[17] GRIFFITHS T L, STEYVERS M. Finding scientific topics[J]. Proceedings of the national academy of sciences, 2004, 101(1):5228-5235.
[18] ZHU C, BYRD R H, LU P, et al. Algorithm 778:L-BFGS-B:Fortran subroutines for large-scale bound-constrained optimization[J]. ACM transactions on mathematical software (TOMS), 1997, 23(4):550-560.
[19] MANNING C D, RAGHAVAN P, SCHVTZE H. Introduction to information retrieval[M]. Cambridge:Cambridge University Press, 2008:1-16.
[20] JIANG D, NG W. Mining web search topics with diverse spatiotemporal patterns[C]//Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval. Dublin, Ireland, 2013:881-884.
[21] LI W, MCCALLUM A. Pachinko allocation:DAG-structured mixture models of topic correlations[C]//Proceedings of the 23rd International Conference on Machine Learning. Pittsburgh, USA, 2006:577-584.
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