research-article

Mining search and browse logs for web search: A Survey

Authors:

Hang LiAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 4, Issue 4

Article No.: 57, Pages 1 - 37

https://doi.org/10.1145/2508037.2508038

Published: 08 October 2013 Publication History

Abstract

Huge amounts of search log data have been accumulated at Web search engines. Currently, a popular Web search engine may receive billions of queries and collect terabytes of records about user search behavior daily. Beside search log data, huge amounts of browse log data have also been collected through client-side browser plugins. Such massive amounts of search and browse log data provide great opportunities for mining the wisdom of crowds and improving Web search. At the same time, designing effective and efficient methods to clean, process, and model log data also presents great challenges.

In this survey, we focus on mining search and browse log data for Web search. We start with an introduction to search and browse log data and an overview of frequently-used data summarizations in log mining. We then elaborate how log mining applications enhance the five major components of a search engine, namely, query understanding, document understanding, document ranking, user understanding, and monitoring and feedback. For each aspect, we survey the major tasks, fundamental principles, and state-of-the-art methods.

References

[1]

Agichtein, E. 2010. Inferring searcher intent. In Proceedings of the International Conference on the World Wide Web (WWW'10).

[2]

Agichtein, E., Brill, E., and Dumais, S. 2006a. Improving Web search ranking by incorporating user behavior information. In Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'06). ACM, New York, NY, 19--26.

Digital Library

[3]

Agichtein, E., Brill, E., Dumais, S., and Ragno, R. 2006b. Learning user interaction models for predicting Web search result preferences. In Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'06). ACM, New York, NY, 3--10.

Digital Library

[4]

Backstrom, L., Kleinberg, J., Kumar, R., and Novak, J. 2008. Spatial variation in search engine queries. In Proceedings of the 17th International Conference on World Wide Web (WWW'08). ACM, New York, NY, 357--366.

Digital Library

[5]

Baeza-Yates, R. 2004. Web mining in search engines. In Proceedings of the 27th Australesion Conference on Computer Science.

Digital Library

[6]

Baeza-Yates, R. A., Calderón-Benavides, L., and González-Caro, C. N. 2006. The intention behind Web queries. In Proceedings of the 13th International Conference on String Processing and Information Retrieval. 98--109.

Digital Library

[7]

Baeza-Yates, R. A. and Ribeiro-Neto, B. 1999. Modern Information Retrieval. Addison-Wesley Longman Publishing Co., Inc., Boston, MA.

Digital Library

[8]

Barbaro, M. and Zeller, T. J. 2006. A face is exposed for AOL searcher no. 4417749. The New York Times.

[9]

Beeferman, D. and Berger, A. 2000. Agglomerative clustering of a search engine query log. In Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'00). ACM, New York, NY, 407--416.

Digital Library

[10]

Beitzel, S. M., Jensen, E. C., Chowdhury, A., Frieder, O., and Grossman, D. 2007. Temporal analysis of a very large topically categorized web query log. J. Ame. Soc. Inf. Sci. Technol. 58, 166--178.

Digital Library

[11]

Beitzel, S. M., Jensen, E. C., Chowdhury, A., Grossman, D., and Frieder, O. 2004. Hourly analysis of a very large topically categorized web query log. In Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'04). ACM, New York, NY, 321--328.

Digital Library

[12]

Beitzel, S. M., Jensen, E. C., Lewis, D. D., Chowdhury, A., and Frieder, O. 2007. Automatic classification of Web queries using very large unlabeled query logs. ACM Trans. Info. Syst. 25, 2.

Digital Library

[13]

Bendersky, M. and Croft, W. B. 2009. Analysis of long queries in a large scale search log. In Proceedings of the Workshop on Web Search Click Data (WSCD'09). ACM, New York, NY, 8--14.

Digital Library

[14]

Bennett, P. N., White, R. W., Chu, W., Dumais, S. T., Bailey, P., Borisyuk, F., and Cui, X. 2012. Modeling the impact of short- and long-term behavior on search personalization. In Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'12). ACM, New York, NY, 185--194.

Digital Library

[15]

Bergsma, S. and Wang, Q. I. 2007. Learning noun phrase query segmentation. In Proceedings of the Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. 819--826.

[16]

Berry, M. W. 2003. Survey of Text Mining. Springer-Verlag, New York, NY.

Digital Library

[17]

Boldi, P., Bonchi, F., Castillo, C., Donato, D., Gionis, A., and Vigna, S. 2008. The query-flow graph: Model and applications. In Proceedings of the 17th ACM Conference on Information and Knowledge Management (CIKM'08). ACM, New York, NY, 609--618.

Digital Library

[18]

Broder, A. 2002. A taxonomy of web search. SIGIR Forum 36, 3--10.

Digital Library

[19]

Cao, H., Jiang, D., Pei, J., Chen, E., and Li, H. 2009. Towards context-aware search by learning a very large variable length hidden Markov model from search logs. In Proceedings of the 18th International Conference on World Wide Web (WWW'09). ACM, New York, NY, 191--200.

Digital Library

[20]

Cao, H., Jiang, D., Pei, J., He, Q., Liao, Z., Chen, E., and Li, H. 2008. Context-aware query suggestion by mining click-through and session data. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'08). ACM, New York, NY, 875--883.

Digital Library

[21]

Chapelle, O. and Zhang, Y. 2009. A dynamic bayesian network click model for Web search ranking. In Proceedings of the 18th International Conference on World Wide Web (WWW'09). ACM, New York, NY, 1--10.

Digital Library

[22]

Chien, S. and Immorlica, N. 2005. Semantic similarity between search engine queries using temporal correlation. In Proceedings of the 14th International Conference on World Wide Web (WWW'05). ACM, New York, NY, 2--11.

Digital Library

[23]

Cleverdon, C. 1967. The cranfield tests on index language devices. Aslib Proc. 19, 173--192.

[24]

Craswell, N. and Szummer, M. 2007. Random walks on the click graph. In Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'07). ACM, New York, NY, 239--246.

Digital Library

[25]

Craswell, N., Zoeter, O., Taylor, M., and Ramsey, B. 2008. An experimental comparison of click position-bias models. In Proceedings of the International Conference on Web Search and Web Data Mining (WSDM'08). ACM, New York, NY, 87--94.

Digital Library

[26]

Croft, W. B., Bendersky, M., Li, H., and Xu, G. 2010. Query representation and understanding workshop. SIGIR Forum 44, 2, 48--53.

Digital Library

[27]

Croft, W. B., Metzler, D., and Strohman, T. 2009. Search Engines - Information Retrieval in Practice. Pearson Education, Lodon, U.K.

Digital Library

[28]

Cui, H., Wen, J.-R., Nie, J.-Y., and Ma, W.-Y. 2002. Probabilistic query expansion using query logs. In Proceedings of the 11th International Conference on World Wide Web (WWW'02). ACM, New York, NY, 325--332.

Digital Library

[29]

Dean, J. and Ghemawat, S. 2004. Mapreduce: Simplified data processing on large clusters. In Proceedings of the 6th Symposium on Operating Systems Design & Implementation. USENIX Association, Berkeley, CA, 10--10.

Digital Library

[30]

Dean, J. and Ghemawat, S. 2008. Mapreduce: Simplified data processing on large clusters. Comm. ACM 51, 107--113.

Digital Library

[31]

Deerwester, S., Dumais, S. T., Furnas, G. W., Landauer, T. K., and Harshman, R. 1990. Indexing by latent semantic analysis. J. Amer. Soc. Inf. Sci. 41, 391--407.

[32]

Dong, A., Chang, Y., Zheng, Z., Mishne, G., Bai, J., Zhang, R., Buchner, K., Liao, C., and Diaz, F. 2010. Towards recency ranking in Web search. In Proceedings of the 3rd ACM International Conference on Web Search and Data Mining (WSDM'10). ACM, New York, NY, 11--20.

Digital Library

[33]

Dou, Z., Song, R., and Wen, J.-R. 2007. A large-scale evaluation and analysis of personalized search strategies. In Proceedings of the 16th International Conference on World Wide Web (WWW'07). ACM, New York, NY, 581--590.

Digital Library

[34]

Dou, Z., Song, R., Yuan, X., and Wen, J.-R. 2008. Are click-through data adequate for learning Web search rankings&quest; In Proceedings of the 17th ACM Conference on Information and Knowledge Management (CIKM'08). ACM, New York, NY, 73--82.

Digital Library

[35]

Duan, H. and Hsu, B.-J. P. 2011. Online spelling correction for query completion. In Proceedings of the 20th International Conference on World Wide Web (WWW'11). ACM, New York, NY, 117--126.

Digital Library

[36]

Dupret, G. E. and Piwowarski, B. 2008. A user browsing model to predict search engine click data from past observations. In Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'08). ACM, New York, NY, 331--338.

Digital Library

[37]

Fonseca, B. M., Golgher, P., Pôssas, B., Ribeiro-Neto, B., and Ziviani, N. 2005. Concept-based interactive query expansion. In Proceedings of the 14th ACM International Conference on Information and Knowledge Management (CIKM'05). ACM, New York, NY, 696--703.

Digital Library

[38]

Fox, S., Karnawat, K., Mydland, M., Dumais, S., and White, T. 2005. Evaluating implicit measures to improve Web search. ACM Trans. Inf. Syst. 23, 147--168.

Digital Library

[39]

Fuxman, A., Tsaparas, P., Achan, K., and Agrawal, R. 2008. Using the wisdom of the crowds for keyword generation. In Proceedings of the 17th International Conference on World Wide Web (WWW'08). ACM, New York, NY, 61--70.

Digital Library

[40]

Gao, J., Yuan, W., Li, X., Deng, K., and Nie, J.-Y. 2009. Smoothing clickthrough data for Web search ranking. In Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'09). ACM, New York, NY, 355--362.

Digital Library

[41]

Guo, F., Liu, C., Kannan, A., Minka, T., Taylor, M. J., Wang, Y. M., and Faloutsos, C. 2009. Click chain model in Web search. In Proceedings of the 18th International Conference on World Wide Web. 11--20.

Digital Library

[42]

Guo, F., Liu, C., and Wang, Y. M. 2009. Efficient multiple-click models in Web search. In Proceedings of the 2nd ACM International Conference on Web Search and Data Mining (WSDM'09). ACM, New York, NY, 124--131.

Digital Library

[43]

Guo, J., Xu, G., Li, H., and Cheng, X. 2008. A unified and discriminative model for query refinement. In Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'08). ACM, New York, NY, 379--386.

Digital Library

[44]

Hagen, M., Potthast, M., Beyer, A., and Stein, B. 2012. Towards optimum query segmentation: In doubt without. In Proceedings of the 21st ACM International Conference on Information and Knowledge Management. 1015--1024.

Digital Library

[45]

Hagen, M., Potthast, M., Stein, B., and Bräutigam, C. 2011. Query segmentation revisited. In Proceedings of the 20th International Conference on World Wide Web. 97--106.

Digital Library

[46]

Hassan, A. 2012. A semi-supervised approach to modeling Web search satisfaction. In Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'12). ACM, New York, NY, 275--284.

Digital Library

[47]

Hassan, A., Jones, R., and Klinkner, K. L. 2010. Beyond DCG: User behavior as a predictor of a successful search. In Proceedings of the 3rd ACM International Conference on Web Search and Data Mining (WSDM'10). ACM, New York, NY, 221--230.

Digital Library

[48]

Haveliwala, T. H. 2002. Topic-sensitive pagerank. In Proceedings of the 11th International Conference on World Wide Web (WWW'02). ACM, New York, NY, 517--526.

Digital Library

[49]

He, D., Göker, A., and Harper, D. J. 2002. Combining evidence for automatic Web session identification. Inf. Process. Manage. 38, 727--742.

Digital Library

[50]

Hölscher, C. and Strube, G. 2000. Web search behavior of internet experts and newbies. In Proceedings of the 9th International World Wide Web Conference on Computer Networks. North-Holland Publishing Co., Amsterdam, The Netherlands, 337--346.

Digital Library

[51]

Hu, B., Zhang, Y., Chen, W., Wang, G., and Yang, Q. 2011. Characterizing search intent diversity into click models. In Proceedings of the 20th International Conference on World Wide Web (WWW'11). ACM, New York, NY, 17--26.

Digital Library

[52]

Hu, J., Wang, G., Lochovsky, F., Sun, J.-T., and Chen, Z. 2009. Understanding user's query intent with wikipedia. In Proceedings of the 18th International Conference on World Wide Web (WWW'09). ACM, New York, NY, 471--480.

Digital Library

[53]

Hu, Y., Qian, Y., Li, H., Jiang, D., Pei, J., and Zheng, Q. 2012. Mining query subtopics from search log data. In Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'12). ACM, New York, NY, 305--314.

Digital Library

[54]

Huang, C.-K., Chien, L.-F., and Oyang, Y.-J. 2003. Relevant term suggestion in interactive Web search based on contextual information in query session logs. J. Amer. Soc. Inf. Sci. Technol. 54, 7, 638--649.

Digital Library

[55]

Jansen, B. J. and Pooch, U. W. 2001. A review of web searching studies and a framework for future research. J. Amer. Soc. Inf. Sci. Technol. 52, 3, 235--246.

Digital Library

[56]

Jansen, B. J. and Spink, A. 2006. How are we searching the World Wide Web&quest;: A comparison of nine search engine transaction logs. Inf. Process. Manage. 42, 248--263.

Digital Library

[57]

Jansen, B. J., Spink, A., Blakely, C., and Koshman, S. 2007. Defining a session on Web search engines: Research articles. J. Amer. Soci. Inf. Sci. Technol. 58, 862--871.

Digital Library

[58]

Ji, M., Yan, J., Gu, S., Han, J., He, X., Zhang, W. V., and Chen, Z. 2011. Learning search tasks in queries and web pages via graph regularization. In Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'11). ACM, New York, NY, 55--64.

Digital Library

[59]

Jin, X., Zhou, Y., and Mobasher, B. 2004. Web usage mining based on probabilistic latent semantic analysis. In Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'04). ACM, New York, NY, 197--205.

Digital Library

[60]

Joachims, T. 2002. Optimizing search engines using clickthrough data. In Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'02). ACM, New York, NY, 133--142.

Digital Library

[61]

Joachims, T., Granka, L., Pan, B., Hembrooke, H., and Gay, G. 2005. Accurately interpreting clickthrough data as implicit feedback. In Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'05). ACM, New York, NY, 154--161.

Digital Library

[62]

Jones, K. S., Walker, S., and Robertson, S. 1998. Probabilistic model of information retrieval: Development and status. Tech. rep. TR-446, Cambridge University Computer Laboratory.

[63]

Jones, R. and Klinkner, K. L. 2008. Beyond the session timeout: Automatic hierarchical segmentation of search topics in query logs. In Proceedings of the 17th ACM Conference on Information and Knowledge Management (CIKM'08). ACM, New York, NY, 699--708.

Digital Library

[64]

Jones, R., Rey, B., Madani, O., and Greiner, W. 2006. Generating query substitutions. In Proceedings of the 15th International Conference on World Wide Web (WWW'06). ACM, New York, NY, 387--396.

Digital Library

[65]

Kang, D., Jiang, D., Pei, J., Liao, Z., Sun, X., and Choi, H.-J. 2011. Multidimensional mining of large-scale search logs: A topic-concept cube approach. In Proceedings of the 4th ACM International Conference on Web Search and Data Mining. 385--394.

Digital Library

[66]

Kleinberg, J. M. 1999. Authoritative sources in a hyperlinked environment. J. ACM 46, 604--632.

Digital Library

[67]

Kulkarni, A., Teevan, J., Svore, K. M., and Dumais, S. T. 2011. Understanding temporal query dynamics. In Proceedings of the 4th ACM International Conference on Web Search and Data Mining (WSDM'11). ACM, New York, NY, 167--176.

Digital Library

[68]

Lafferty, J. and Zhai, C. 2001. Document language models, query models, and risk minimization for information retrieval. In Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'01). ACM, New York, NY, 111--119.

Digital Library

[69]

Lathauwer, L. D., Moor, B. D., and Vandewalle, J. 2000. A multilinear singular value decomposition. SIAM J. Matrix Anal. Appl. 21, 4, 1253--1278.

Digital Library

[70]

Lee, U., Liu, Z., and Cho, J. 2005. Automatic identification of user goals in web search. In Proceedings of the 14th International Conference on World Wide Web (WWW'05). ACM, New York, NY, 391--400.

Digital Library

[71]

Li, H. 2011. Learning to rank for information retrieval and natural language processing. Synthesis Lect. Human Lan. Technol. 4, 1, 1--113.

Digital Library

[72]

Li, X., Wang, Y.-Y., and Acero, A. 2008. Learning query intent from regularized click graphs. In Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'08). ACM, New York, NY, 339--346.

Digital Library

[73]

Li, Y., Duan, H., and Zhai, C. 2012. A generalized hidden Markov model with discriminative training for query spelling correction. In Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'12). ACM, New York, NY, 611--620.

Digital Library

[74]

Li, Y., Hsu, B.-J. P., Zhai, C., and Wang, K. 2011. Unsupervised query segmentation using clickthrough for information retrieval. In Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'11). ACM, New York, NY, 285--294.

Digital Library

[75]

Liao, Z., Jiang, D., Chen, E., Pei, J., Cao, H., and Li, H. 2011. Mining concept sequences from large-scale search logs for context-aware query suggestion. ACM Trans. Intell. Syst. Technol. 3, 1, 17.

Digital Library

[76]

Liao, Z., Song, Y., He, L.-w., and Huang, Y. 2012. Evaluating the effectiveness of search task trails. In Proceedings of the 21st International Conference on World Wide Web (WWW'12). ACM, New York, NY, 489--498.

Digital Library

[77]

Liu, C., Guo, F., and Faloutsos, C. 2009. Bbm: Bayesian browsing model from petabyte-scale data. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'09). ACM, New York, NY, 537--546.

Digital Library

[78]

Liu, F., Yu, C., and Meng, W. 2002. Personalized Web search by mapping user queries to categories. In Proceedings of the 11th International Conference on Information and Knowledge Management (CIKM'02). ACM, New York, NY, 558--565.

Digital Library

[79]

Liu, Y., Gao, B., Liu, T.-Y., Zhang, Y., Ma, Z., He, S., and Li, H. 2008. Browserank: Letting Web users vote for page importance. In Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'08). ACM, New York, NY, 451--458.

Digital Library

[80]

Lucchese, C., Orlando, S., Perego, R., Silvestri, F., and Tolomei, G. 2011. Identifying task-based sessions in search engine query logs. In Proceedings of the 4th ACM International Conference on Web Search and Data Mining (WSDM'11). ACM, New York, NY, 277--286.

Digital Library

[81]

Matthijs, N. and Radlinski, F. 2011. Personalizing Web search using long term browsing history. In Proceedings of the 4th ACM International Conference on Web Search and Data Mining. 25--34.

Digital Library

[82]

Mei, Q. and Church, K. 2008. Entropy of search logs: How hard is search&quest; With personalization&quest; With backoff&quest; In Proceedings of the International Conference on Web Search and Web Data Mining (WSDM'08). ACM, New York, NY, 45--54.

Digital Library

[83]

Mei, Q., Zhou, D., and Church, K. 2008. Query suggestion using hitting time. In Proceedings of the 17th ACM Conference on Information and Knowledge Management (CIKM'08). ACM, New York, NY, 469--478.

Digital Library

[84]

Ozertem, U., Chapelle, O., Donmez, P., and Velipasaoglu, E. 2012. Learning to suggest: A machine learning framework for ranking query suggestions. In Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'12). ACM, New York, NY, 25--34.

Digital Library

[85]

Paşca, M. 2007. Organizing and searching the World Wide Web of facts -- step two: Harnessing the wisdom of the crowds. In Proceedings of the 16th International Conference on World Wide Web (WWW'07). ACM, New York, NY, 101--110.

Digital Library

[86]

Paşca, M. and Alfonseca, E. 2009. Web-derived resources for Web information retrieval: From conceptual hierarchies to attribute hierarchies. In Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'09). ACM, New York, NY, 596--603.

Digital Library

[87]

Page, L., Brin, S., Motwani, R., and Winograd, T. 1999. The pagerank citation ranking: Bringing order to the Web. Tech. rep. 1999--66, Stanford InfoLab.

[88]

Pitkow, J., Schütze, H., Cass, T., Cooley, R., Turnbull, D., Edmonds, A., Adar, E., and Breuel, T. 2002. Personalized search. Comm. ACM 45, 9, 50--55.

Digital Library

[89]

Piwowarski, B., Dupret, G., and Jones, R. 2009. Mining user Web search activity with layered bayesian networks or how to capture a click in its context. In Proceedings of the 2nd ACM International Conference on Web Search and Data Mining (WSDM'09). ACM, New York, NY, 162--171.

Digital Library

[90]

Poblete, B. and Baeza-Yates, R. 2008. Query-sets: Using implicit feedback and query patterns to organize Web documents. In Proceedings of the 17th International Conference on World Wide Web (WWW'08). ACM, New York, NY, 41--50.

Digital Library

[91]

Pretschner, A. and Gauch, S. 1999. Ontology based personalized search. In Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'99). IEEE Computer Society, Los Alamitos, CA, 391.

Digital Library

[92]

Qiu, F. and Cho, J. 2006. Automatic identification of user interest for personalized search. In Proceedings of the 15th International Conference on World Wide Web (WWW'06). ACM, New York, NY, 727--736.

Digital Library

[93]

Radlinski, F. and Joachims, T. 2005. Query chains: Learning to rank from implicit feedback. In Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining (KDD'05). ACM, New York, NY, 239--248.

Digital Library

[94]

Radlinski, F. and Joachims, T. 2006. Minimally invasive randomization for collecting unbiased preferences from clickthrough logs. In Proceedings of the 21st National Conference on Artificial Intelligence. AAAI Press, 1406--1412.

Digital Library

[95]

Radlinski, F. and Joachims, T. 2007. Active exploration for learning rankings from clickthrough data. In Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'07). ACM, New York, NY, 570--579.

Digital Library

[96]

Rose, D. E. and Levinson, D. 2004. Understanding user goals in Web search. In Proceedings of the 13th International Conference on World Wide Web (WWW'04). ACM, New York, NY, 13--19.

Digital Library

[97]

Salton, G., Wong, A., and Yang, C. S. 1975. A vector space model for automatic indexing. Comm. ACM 18, 613--620.

Digital Library

[98]

Shen, D., Pan, R., Sun, J.-T., Pan, J. J., Wu, K., Yin, J., and Yang, Q. 2006. Query enrichment for web-query classification. ACM Trans. Inf. Syst. 24, 3, 320--352.

Digital Library

[99]

Shen, D., Sun, J.-T., Yang, Q., and Chen, Z. 2006. Building bridges for Web query classification. In Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'06). ACM, New York, NY, 131--138.

Digital Library

[100]

Shen, X., Tan, B., and Zhai, C. 2005a. Context-sensitive information retrieval using implicit feedback. In Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'05). ACM, New York, NY, 43--50.

Digital Library

[101]

Shen, X., Tan, B., and Zhai, C. 2005b. Implicit user modeling for personalized search. In Proceedings of the 14th ACM International Conference on Information and Knowledge Management (CIKM'05). ACM, New York, NY, 824--831.

Digital Library

[102]

Shokouhi, M. and Radinsky, K. 2012. Time-sensitive query auto-completion. In Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'12). ACM, New York, NY, 601--610.

Digital Library

[103]

Silverstein, C., Marais, H., Henzinger, M., and Moricz, M. 1999. Analysis of a very large web search engine query log. SIGIR Forum 33, 6--12.

Digital Library

[104]

Silvestri, F. 2010. Mining query logs: Turning search usage data into knowledge. Found. Trends Inf. Retriev. 4, 1--2, 1--174.

Digital Library

[105]

Speretta, M. and Gauch, S. 2005. Personalized search based on user search histories. In Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence (WI'05). IEEE Computer Society, Washington, DC, 622--628.

Digital Library

[106]

Spink, A., Jansen, B. J., Wolfram, D., and Saracevic, T. 2002. From e-sex to e-commerce: Web search changes. Computer 35, 107--109.

Digital Library

[107]

Spink, A., Wolfram, D., Jansen, M. B. J., and Saracevic, T. 2001. Searching the Web: The public and their queries. J. Amer. Soc. Inf. Sci. Technol. 52, 3, 226--234.

Digital Library

[108]

Sun, J.-T., Zeng, H.-J., Liu, H., Lu, Y., and Chen, Z. 2005. Cubesvd: A novel approach to personalized Web search. In Proceedings of the 14th International Conference on World Wide Web. 382--390.

Digital Library

[109]

Szpektor, I., Gionis, A., and Maarek, Y. 2011. Improving recommendation for long-tail queries via templates. In Proceedings of the 20th International Conference on World Wide Web (WWW'11). ACM, New York, NY, 47--56.

Digital Library

[110]

Tan, B., Shen, X., and Zhai, C. 2006. Mining long-term search history to improve search accuracy. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'06). ACM, New York, NY, 718--723.

Digital Library

[111]

Teevan, J., Adar, E., Jones, R., and Potts, M. A. S. 2007. Information re-retrieval: Repeat queries in Yahoo's logs. In Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'07). ACM, New York, NY, 151--158.

Digital Library

[112]

Teevan, J., Dumais, S. T., and Horvitz, E. 2005. Personalizing search via automated analysis of interests and activities. In Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'05). ACM, New York, NY, 449--456.

Digital Library

[113]

Teevan, J., Dumais, S. T., and Liebling, D. J. 2008. To personalize or not to personalize: Modeling queries with variation in user intent. In Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'08). ACM, New York, NY, 163--170.

Digital Library

[114]

Tyler, S. K. and Teevan, J. 2010. Large scale query log analysis of re-finding. In Proceedings of the 3rd ACM International Conference on Web Search and Data Mining. 191--200.

Digital Library

[115]

Vlachos, M., Meek, C., Vagena, Z., and Gunopulos, D. 2004. Identifying similarities, periodicities and bursts for online search queries. In Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD'04). ACM, New York, NY, 131--142.

Digital Library

[116]

Wang, K., Gloy, N., and Li, X. 2010. Inferring search behaviors using partially observable markov (pom) model. In Proceedings of the 3rd ACM International Conference on Web Search and Data Mining (WSDM'10). ACM, New York, NY, 211--220.

Digital Library

[117]

Weber, I. and Jaimes, A. 2011. Who uses Web search for what&quest; And how&quest; In Proceedings of the 4th ACM International Conference on Web Search and Data Mining (WSDM'11). ACM, New York, NY, 15--24.

Digital Library

[118]

Wedig, S. and Madani, O. 2006. A large-scale analysis of query logs for assessing personalization opportunities. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'06). ACM, New York, NY, 742--747.

Digital Library

[119]

Weerkamp, W., Berendsen, R., Kovachev, B., Meij, E., Balog, K., and de Rijke, M. 2011. People searching for people: Analysis of a people search engine log. In Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'11). ACM, New York, NY, 45--54.

Digital Library

[120]

Welch, M. J. and Cho, J. 2008. Automatically identifying localizable queries. In Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'08). ACM, New York, NY, 507--514.

Digital Library

[121]

Wen, J.-R., Nie, J.-Y., and Zhang, H.-J. 2001. Clustering user queries of a search engine. In Proceedings of the 10th International Conference on World Wide Web (WWW'01). ACM, New York, NY, 162--168.

Digital Library

[122]

White, R. W., Bailey, P., and Chen, L. 2009. Predicting user interests from contextual information. In Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'09). ACM, New York, NY, 363--370.

Digital Library

[123]

Wolfram, D., Spink, A., Jansen, B. J., and Saracevic, T. 2001. Vox populi: The public searching of the web. J. Amer. Soci. Inf. Sci. Technol. 52, 12, 1073--1074.

Digital Library

[124]

Xiang, B., Jiang, D., Pei, J., Sun, X., Chen, E., and Li, H. 2010. Context-aware ranking in web search. In Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'10). ACM, New York, NY, 451--458.

Digital Library

[125]

Xu, J. and Xu, G. 2011. Learning similarity function for rare queries. In Proceedings of the 4th ACM International Conference on Web Search and Data Mining (WSDM'11). ACM, New York, NY, 615--624.

Digital Library

[126]

Xue, G.-R., Zeng, H.-J., Chen, Z., Yu, Y., Ma, W.-Y., Xi, W., and Fan, W. 2004. Optimizing Web search using Web click-through data. In Proceedings of the 13th ACM International Conference on Information and Knowledge Management (CIKM'04). ACM, New York, NY, 118--126.

Digital Library

[127]

Yi, X., Raghavan, H., and Leggetter, C. 2009. Discovering users' specific geo intention in Web search. In Proceedings of the 18th International Conference on World Wide Web (WWW'09). ACM, New York, NY, 481--490.

Digital Library

[128]

Zhu, G. and Mishne, G. 2009. Mining rich session context to improve Web search. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'09). ACM, New York, NY, 1037--1046.

Digital Library

Cited By

Lafia SMillion AHemphill L(2024)Exploratory and directed search strategies at a social science data archiveIASSIST Quarterly10.29173/iq108748:1Online publication date: 28-Mar-2024
https://doi.org/10.29173/iq1087
Farshidi SRezaee KMazaheri SRahimi ADadashzadeh AZiabakhsh MEskandari SJansen S(2024)Understanding user intent modeling for conversational recommender systems: a systematic literature reviewUser Modeling and User-Adapted Interaction10.1007/s11257-024-09398-x34:5(1643-1706)Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1007/s11257-024-09398-x
Piccardi TGerlach MArora AWest R(2023)A Large-Scale Characterization of How Readers Browse WikipediaACM Transactions on the Web10.1145/358031817:2(1-22)Online publication date: 3-Apr-2023
https://dl.acm.org/doi/10.1145/3580318
Show More Cited By

Index Terms

Mining search and browse logs for web search: A Survey
1. Information systems
  1. Information retrieval
    1. Information retrieval query processing

Recommendations

Enhancing web search by mining search and browse logs
SIGIR '11: Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval

Huge amounts of search log data have been accumulated in various search engines. Currently, a commercial search engine receives billions of queries and collects tera-bytes of log data on any single day. Other than search log data, browse logs can be ...
Web search/browse log mining: challenges, methods, and applications
WWW '10: Proceedings of the 19th international conference on World wide web

Huge amounts of search and browse log data has been accumulated in various search engines. Such massive search/browse log data, on the one hand, provides great opportunities to mine the wisdom of crowds and improve Web search as well as online ...
Improving Ranking Consistency for Web Search by Leveraging a Knowledge Base and Search Logs
CIKM '15: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management

In this paper, we propose a new idea called ranking consistency in web search. Relevance ranking is one of the biggest problems in creating an effective web search system. Given some queries with similar search intents, conventional approaches typically ...

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 4, Issue 4

Survey papers, special sections on the semantic adaptive social web, intelligent systems for health informatics, regular papers

September 2013

452 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/2508037

Issue’s Table of Contents

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 October 2013

Accepted: 01 March 2013

Revised: 01 February 2013

Received: 01 October 2012

Published in TIST Volume 4, Issue 4

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Survey
Refereed

Funding Sources

Natural Sciences and Engineering Research Council of Canada

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

38
Total Citations
View Citations
1,164
Total Downloads

Downloads (Last 12 months)16
Downloads (Last 6 weeks)3

Reflects downloads up to 19 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Lafia SMillion AHemphill L(2024)Exploratory and directed search strategies at a social science data archiveIASSIST Quarterly10.29173/iq108748:1Online publication date: 28-Mar-2024
https://doi.org/10.29173/iq1087
Farshidi SRezaee KMazaheri SRahimi ADadashzadeh AZiabakhsh MEskandari SJansen S(2024)Understanding user intent modeling for conversational recommender systems: a systematic literature reviewUser Modeling and User-Adapted Interaction10.1007/s11257-024-09398-x34:5(1643-1706)Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1007/s11257-024-09398-x
Piccardi TGerlach MArora AWest R(2023)A Large-Scale Characterization of How Readers Browse WikipediaACM Transactions on the Web10.1145/358031817:2(1-22)Online publication date: 3-Apr-2023
https://dl.acm.org/doi/10.1145/3580318
Urman AMakhortykh M(2023)You are how (and where) you search? Comparative analysis of web search behavior using web tracking dataJournal of Computational Social Science10.1007/s42001-023-00208-96:2(741-756)Online publication date: 3-May-2023
https://doi.org/10.1007/s42001-023-00208-9
Iwanaga JNishimura NSukegawa NTakano Y(2022)Analysis of Information Search around the Time of Childbirth: Estimating Probability Distributions of Search Dates via Mathematical Optimization出産前後の情報検索の分析：数理最適化による検索日の確率分布推定Transactions of the Japanese Society for Artificial Intelligence10.1527/tjsai.37-3_D-L7437:3(D-L74_1-11)Online publication date: 1-May-2022
https://doi.org/10.1527/tjsai.37-3_D-L74
Ibáñez LSimperl EElsweiler DKruschwitz ULudwig B(2022)A comparison of dataset search behaviour of internal versus search engine referred sessionsProceedings of the 2022 Conference on Human Information Interaction and Retrieval10.1145/3498366.3505821(158-168)Online publication date: 14-Mar-2022
https://dl.acm.org/doi/10.1145/3498366.3505821
Kamara SKati AMoataz TSchneider TTreiber AYonli M(2022)SoK: Cryptanalysis of Encrypted Search with LEAKER – A framework for LEakage AttacK Evaluation on Real-world data2022 IEEE 7th European Symposium on Security and Privacy (EuroS&P)10.1109/EuroSP53844.2022.00014(90-108)Online publication date: Jun-2022
https://doi.org/10.1109/EuroSP53844.2022.00014
Avron UGershtein SGuy IMilo TNovgorodov S(2021)ConCaT: Construction of Category Trees from Search Queries in E-Commerce2021 IEEE 37th International Conference on Data Engineering (ICDE)10.1109/ICDE51399.2021.00308(2701-2704)Online publication date: Apr-2021
https://doi.org/10.1109/ICDE51399.2021.00308
Bu XZhu JQian X(2020)Personalized product search based on user transaction history and hypergraph learningMultimedia Tools and Applications10.1007/s11042-020-08963-xOnline publication date: 17-May-2020
https://doi.org/10.1007/s11042-020-08963-x
Srinivas KRajeshwar J(2020)Identifying User’s Interest in Using E-Payment SystemsInnovations in Computer Science and Engineering10.1007/978-981-15-2043-3_40(353-361)Online publication date: 4-Mar-2020
https://doi.org/10.1007/978-981-15-2043-3_40
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Issue’s Table of Contents