JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2017, Vol. 52 ›› Issue (9): 26-34.doi: 10.6040/j.issn.1671-9352.1.2016.047

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User preference prediction in heterogeneous search environment

ZHANG Fan, LUO Cheng, LIU Yi-qun, ZHANG Min, MA Shao-ping   

  1. State Key Laboratory of Intelligent Technology and Systems, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
  • Received:2016-11-25 Online:2017-09-20 Published:2017-09-15

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Abstract: We propose a series of SERP based features for learning classifiers to automatically predict user preference and we attempt to combine prediction model and user study. The experimental results show that it is difficult to make reliable prediction for user preference only based on appearances of SERPs despite the abundant information of SERPs in heterogeneous environment.

Key words: heterogeneous environments, user preferences, automatic prediction

CLC Number: 

  • TP391
[1] CLEVERDON Cyril, MILLS, Jack, KEEN Michael. Aslib Cranfield research project-Factors determining the performance of indexing systems[J]. Volume I-Design, Volume II-Test Results, AS, 1966, 135(4):892-894.
[2] SANDERSON M, PARAMITA M L, CLOUGH P, et al. Do user preferences and evaluation measures line up?[C] // Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2010: 555-562.
[3] HASSAN AWADALLAH A, ZITOUNI I. Machine-assisted search preference evaluation[C] // Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management. New York: ACM, 2014: 51-60.
[4] LIU Z, LIU Y, ZHOU K, et al. Influence of vertical result in web search examination[C] // Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2015: 193-202.
[5] JÄRVELIN K, KEKÄLÄINEn J. Cumulated gain-based evaluation of IR techniques[J]. ACM Transactions on Information Systems(TOIS), 2002, 20(4): 422-446.
[6] MOFFAT A, ZOBEL J. Rank-biased precision for measurement of retrieval effectiveness[J]. ACM Transactions on Information Systems(TOIS), 2008, 27(1): 1-27.
[7] SMUCKER M D, CLARKE C L A. Time-based calibration of effectiveness measures[C] // Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2012: 95-104.
[8] SAKAI T, DOU Z. Summaries, ranked retrieval and sessions: A unified framework for information access evaluation[C] // Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2013: 473-482.
[9] AL-MASKARI A, SANDERSON M, CLOUGH P. The relationship between IR effectiveness measures and user satisfaction[C] // Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2007: 773-774.
[10] JOACHIMS T. Evaluating retrieval performance using clickthrough data[J]. Text Mining, 2002, 57:79-96.
[11] THOMAS P, HAWKING D. Evaluation by comparing result sets in context[C] // Proceedings of the 15th ACM International Conference on Information and Knowledge Management. New York: ACM, 2006: 94-101.
[12] FOX S, KARNAWAT K, MYDLAND M, et al. Evaluating implicit measures to improve web search[J]. ACM Transactions on Information Systems(TOIS), 2005, 23(2):147-168.
[13] KIM Y, HASSAN A, WHITE R W, et al. Modeling dwell time to predict click-level satisfaction[C] // Proceedings of the 7th ACM International Conference on Web Search and Data Mining. New York: ACM, 2014: 193-202.
[14] LIU Y, CHEN Y, TANG J, et al. Different users, different opinions: Predicting search satisfaction with mouse movement information[C] // Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2015: 493-502.
[15] KIM J, KAZAI G, ZITOUNI I. Relevance dimensions in preference-based IR evaluation[C] // Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2013: 913-916.
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