Skip to main content
SpringerLink
Log in

User behavior modeling for better Web search ranking

  • Review Article
  • Published:
Frontiers of Computer Science Aims and scope Submit manuscript

Abstract

Modern search engines record user interactions and use them to improve search quality. In particular, user click-through has been successfully used to improve clickthrough rate (CTR), Web search ranking, and query recommendations and suggestions. Although click-through logs can provide implicit feedback of users’ click preferences, deriving accurate absolute relevance judgments is difficult because of the existence of click noises and behavior biases. Previous studies showed that user clicking behaviors are biased toward many aspects such as “position” (user’s attention decreases from top to bottom) and “trust” (Web site reputations will affect user’s judgment). To address these problems, researchers have proposed several behavior models (usually referred to as click models) to describe users? practical browsing behaviors and to obtain an unbiased estimation of result relevance. In this study, we review recent efforts to construct click models for better search ranking and propose a novel convolutional neural network architecture for building click models. Compared to traditional click models, our model not only considers user behavior assumptions as input signals but also uses the content and context information of search engine result pages. In addition, our model uses parameters from traditional click models to restrict the meaning of some outputs in our model’s hidden layer. Experimental results show that the proposed model can achieve considerable improvement over state-of-the-art click models based on the evaluation metric of click perplexity.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Joachims T, Granka L, Pan B, Hembrooke H, Gay G. Accurately interpreting clickthrough data as implicit feedback. In: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 2005, 154–161

    Google Scholar 

  2. Craswell N, Zoeter O, Taylor M, Ramsey B. An experimental comparison of click position-bias models. In: Proceedings of ACM International Conference on Web Search and Data Mining. 2008, 87–94

    Google Scholar 

  3. Yue Y S, Patel R, Roehrig H. Beyond position bias: examining result attractiveness as a source of presentation bias in clickthrough data. In: Proceedings of the 19th ACM International Conference onWorldWide Web. 2010, 1011–1018

    Google Scholar 

  4. Wang C, Liu Y Q, Zhang M, Ma S P, Zheng M H, Qian J, Zhang K. Incorporating vertical results into search click models. In: Proceedings of the 36th ACM International ACM SIGIR Conference on Research and Development in Information Retrieval. 2013, 503–512

    Google Scholar 

  5. Guo F, Liu C, Wang Y M. Efficient multiple-click models in web search. In: Proceedings of the 2nd ACM International Conference on Web Search and Data Mining. 2009, 124–131

    Chapter  Google Scholar 

  6. Dupret G E, Piwowarski B. 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. 2008, 331–338

    Google Scholar 

  7. Chapelle O, Zhang Y. A dynamic Bayesian network click model for Web search ranking. In: Proceedings of the 18th ACM International Conference on World Wide Web. 2009, 1–10

    Google Scholar 

  8. Liu Z Y, Liu YQ, Zhou K, ZhangM, Ma S P. Influence of vertical result in Web search examination. In: Proceedings of the 38th International ACMSIGIR Conference on Research and Development in Information Retrieval. 2015, 193–202

    Google Scholar 

  9. Wang C, Liu Y Q, Wang M, Zhou K, Nie J Y, Ma S P. Incorporating non-sequential behavior into click models. In: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2013, 283–292

    Google Scholar 

  10. Kaisser M, Hearst M, Lowe J. Improving search results quality by customizing summary lengths. In: Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics: Human Language Technlogies (ACLHLT’08). 2008

    Google Scholar 

  11. Kanungo T, Orr D. Predicting the readability of short Web summaries. In: Proceedings of the International Conference on Web Search and Web Data Mining. 2009, 325–326

    Google Scholar 

  12. Carbonell J, Goldstein J. The use of MMR, diversity-based reranking for reordering documents and producing summaries. In: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 1998, 335–336

    Google Scholar 

  13. Clarke C L A, Kolla M, Cormack G V, Vechtomova O, Ashkan A, Büttcher S, MacKinnon I. Novelty and diversity in information retrieval evaluation. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 2008, 659–666

    Google Scholar 

  14. Wang H N, Zhai C X, Dong A L, Chang Y. Content-aware click modeling. In: Proceedings of the 23rd International World-Wide Web Conference. 2013, 175–176

    Google Scholar 

  15. Schmidhuber J. Deep learning in neural networks: an overview. Neural Networks, 2015, 61: 85–117

    Article  Google Scholar 

  16. Yu L, Hermann K N, Blunsom P, Pulman S. Deep learning for answer sentence selection. In: Proceedings of NIPS Deep Learning and Representation Learning Workshop. 2014, 393–402

    Google Scholar 

  17. Kim Y. Convolutional neural networks for sentence classification. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2014, 1746–1751

    Chapter  Google Scholar 

  18. Huang P S, He X D, Gao J F, Deng L, Acero A, Heck L. Learning deep structured semantic models for Web search using clickthrough data. In: Proceedings of the 22nd ACM International Conference on Information and Knowledge Management. 2013, 2333–2338

    Google Scholar 

  19. Severyn A, Moschitti A. Learning to rank short text pairs with convolutional deep neural networks. In: Proceedings of the 38th International ACMSIGIR Conference on Research and Development in Information Retrieval. 2015, 373–382

    Google Scholar 

  20. Liu Q, Yu F, Wu S, Wang L. A convolutional click prediction model. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. 2015, 1743–1746

    Google Scholar 

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

    Google Scholar 

  22. Buscher G, Van Elst L, Dengel A. Segment-level display time as implicit feedback: a comparison to eye tracking. In: Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval. 2009, 67–74

    Google Scholar 

  23. Smucker M D, Clarke C L A. Time-based calibration of effectiveness measures. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2012, 95–104

    Google Scholar 

  24. Fox S, Karnawat K, Mydland M, Dumais S, White T. Evaluating implicit measures to improve Web search. ACM Transactions on Information Systems, 2005, 23(2): 147–168

    Article  Google Scholar 

  25. White R W, Kelly D. A study on the effects of personalization and task information on implicit feedback performance. In: Proceedings of the 15th ACM International Conference on Information and Knowledge Management. 2006, 297–306

    Google Scholar 

  26. Agichtein E, Brill E, Dumais S. 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. 2006, 19–26

    Google Scholar 

  27. Xu W H, Manavoglu E, Cantu-Paz E. Temporal click model for sponsored search. In: Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 2010, 106–113

    Google Scholar 

  28. Wang K S, Gloy N, Li X L. Inferring search behaviors using partially observable Markov (POM) model. In: Proceedings of the 3rd ACM International Conference on Web Search and Data Mining. 2010, 211–220

    Chapter  Google Scholar 

  29. Xu D Q, Liu Y Q, Zhang M, Ma S P, Ru L Y. Incorporating revisiting behaviors into click models. In: Proceedings of the 5th ACM International Conference on Web Search and Data Mining. 2012, 303–312

    Google Scholar 

  30. Liu Y Q, Xie X H, Wang C, Nie J Y, Zhang M, Ma S P. Time-aware click model. ACM Transactions on Information Systems, 2016, 35(3): 24–34

    Article  Google Scholar 

  31. Salakhutdinov R, Hinton G. Semantic hashing. International Journal of Approximate Reasoning, 2009, 50(7): 969–978

    Article  Google Scholar 

  32. Socher R, Huval B, Manning C D, Ng A Y. Semantic compositionality through recursive matrix-vector spaces. In: Proceedings of the Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. 2012, 1201–1211

    Google Scholar 

  33. Tur G, Deng L, Hakkani-Tür D, He X D. Towards deeper understanding: deep convex networks for semantic utterance classification. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing. 2012, 5045–5048

    Google Scholar 

  34. Shen Y L, He X D, Gao J F, Deng L, Mesnil G. Learning semantic representations using convolutional neural networks for Web search. In: Proceedings of the 23rd International Conference onWorld WideWeb. 2014, 373–374

    Google Scholar 

  35. Zhang Y Y, Dai H J, Xu C, Feng J, Wang T F, Bian J, Wang B, Liu T Y. Sequential click prediction for sponsored search with recurrent neural networks. In: Proceedings of the 28th AAAI Conference on Artificial Intelligence. 2014, 133–134

    Google Scholar 

  36. Borisov A, Markov I, de Rijke M, Serdyukov P. A neural click model for Web search. In: Proceedings of the 25th International Conference on World Wide Web. 2016, 531–541

    Chapter  Google Scholar 

  37. Kalchbrenner N, Grefenstette E, Blunsom P. A convolutional neural network for modelling sentences. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics. 2014

    Google Scholar 

  38. Mikolov T, Sutskever I, Chen K, Corrado G S, Dean J. Distributed representations of words and phrases and their compositionality. In: Proceedings of the Neural Information Processing Systems Conference. 2013, 3111–3119

    Google Scholar 

  39. Nair V, Hinton G E. Rectified linear units improve restricted boltzmann machines. In: Proceedings of the 27th International Conference onMachine Learning. 2010, 807–814

    Google Scholar 

  40. Wan L, Zeiler M, Zhang S X, Cun Y L, Fergus R. Regularization of neural networks using dropconnect. In: Proceedings of the 30th International Conference on Machine Learning. 2013, 1058–1066

    Google Scholar 

  41. Bordes A, Weston J, Usunier N. Open question answering with weakly supervised embedding models. In: Proceedings of Joint European Conference on Machine Learning and Knowledge Discovery in Databases. 2014, 165–180

    Google Scholar 

  42. Echihabi A, Marcu D. A noisychannel approach to question answering. In: Proceedings of the 41st Annual Meeting on Association for Computational Linguistics. 2003, 16–23

    Google Scholar 

  43. Chen D Q, Chen W Z, Wang J X, Chen Z, Yang Q. Beyond ten blue links: enabling user click modeling in federated web search. In: Proceedings of the 5th ACM International Conference on Web Search and Data Mining. 2012, 463–472

    Google Scholar 

  44. Järvelin K, Kekäläinen J. Cumulated gain-based evaluation of IR techniques. ACM Transactions on Information Systems, 2002, 20(4): 422–446

    Article  Google Scholar 

  45. Yang H, Mityagin A, Svore K M, Markov S. Collecting high quality overlapping labels at low cost. In Proceedings of the 33rd International ACMSIGIR Conference on Research and Development in Information Retrieval. 2010, 459–466

    Google Scholar 

  46. Landis J R, Koch G G. The measurement of observer agreement for categorical data. Biometrics, 1977, 33(1): 159–174

    Article  MATH  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 61622208, 61732008, 61532011). It is also partly supported by Tsinghua University Initiative Scientific Research Program (2014Z21032) and the National Key Basic Research Program of China (973 Program) (2015CB358700).

Author information

Authors and Affiliations

  1. Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China

    Yiqun Liu, Chao Wang, Min Zhang & Shaoping Ma

Authors
  1. Yiqun Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chao Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Min Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shaoping Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yiqun Liu.

Additional information

Yiqun Liu is an associate professor at the Department of Computer Science and Technology, Tsinghua University, China. His major research interests are in Web search, user behavior analysis, and natural language processing. He serves as co- Editor-in-chief of Frontiers and Trends of Information Retrieval (FnTIR), Program Co-chair of SIGIR2018, Short Paper Co-chair of SIGIR2017, Program Co-chair of NTCIR-13, General Co-chair of AIRS2016 as well as (senior) program committee members of a number of important international academic conferences including SIGIR, WWW, AAAI, ACL and IJCAI. In 2016, he was supported by NSFC as an Outstanding Young Scholar (2017–2019).

Chao Wang is now working as a researcher at Baidu.com since he obtained his PhD degree from TsinghuaUniversity, China in 2016. His major research interests are in Web search and user behavior analysis. He has published a number of high quality papers in top-tier academic conference and journals such as SIGIR, CIKM and TOIS. He also received the best paper honorable mention award of SIGIR2015

Min Zhang is an associate professor in the Department of Computer Science and Technology, Tsinghua University, China. She specializes in Web search and recommendation and Web user modeling. Currently she is also the vice director of State Key Lab. of Intelligent Technology and Systems, the executive director of Tsinghua University-Microsoft Research Asia Joint Research Lab on Media and Search. She also serves as associate editor for the ACM Transaction on Information Systems (TOIS), Program co-Chair ofWSDM2017 and AIRS 2016, area chairs or senior PC members at SIGIR, CIKM, and PC members at WWW, IJCAI, KDD, AAAI, ACL, etc. She has published more than 70 papers in important international journals and conferences, and 12 of her patents are filed. She was awarded Beijing Science and Technology Award (First Prize) in 2016.

Shaoping Ma is a professor in the Department of Computer Science and Technology, Tsinghua University, China. He is also the director of "Tsinghua-Sogou" Joint Lab on Web search technology research, acting vice director of “Tiangong” Research Institute of Intelligent Computing of Tsinghua University and vice president of CAAI. He is interested in the research areas of intelligent information processing, information retrieval, and Web data mining. He has been recently focusing on improving search performance with the help of semantic mining of user behaviors.

Electronic supplementary material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, Y., Wang, C., Zhang, M. et al. User behavior modeling for better Web search ranking. Front. Comput. Sci. 11, 923–936 (2017). https://doi.org/10.1007/s11704-017-6518-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11704-017-6518-6

Keywords

Search

Navigation