您的位置：山东大学 -> 科技期刊社 -> 《山东大学学报（理学版）》

山东大学学报（理学版） ›› 2017, Vol. 52 ›› Issue (9): 26-34.doi: 10.6040/j.issn.1671-9352.1.2016.047

• • 上一篇    下一篇

异质搜索环境下的用户偏好性预测方法研究

张帆,罗成,刘奕群,张敏,马少平   

  1. 清华大学计算机系, 智能技术与系统国家重点实验室, 北京 100084
  • 收稿日期:2016-11-25 出版日期:2017-09-20 发布日期:2017-09-15
  • 作者简介:张帆(1994— ),男,硕士研究生,研究方向为信息检索. E-mail:frankyzf94@gmail.com
  • 基金资助:
    国家自然科学基金资助项目(61622208,61532011,61472206);国家重点基础研究发展计划(973计划)资助项目(2015CB358700)

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

PDF (PC)

428

摘要： 提出了一系列基于搜索结果页面的特征用于学习分类器,自动预测用户的偏好性,并尝试将预测模型与用户实验结合起来。实验结果表明,尽管异质环境下搜索结果页面有着丰富的信息,但仅基于搜索结果页面的展现形式难以对用户的偏好性做出可靠的预测。

关键词: 异质环境, 用户偏好性, 自动预测

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

中图分类号: 

  • 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.
[1] 龚双双,陈钰枫,徐金安,张玉洁. 基于网络文本的汉语多词表达抽取方法[J]. 山东大学学报（理学版）, 2018, 53(9): 40-48.
[2] 余传明,左宇恒,郭亚静,安璐. 基于复合主题演化模型的作者研究兴趣动态发现[J]. 山东大学学报（理学版）, 2018, 53(9): 23-34.
[3] 严倩,王礼敏,李寿山,周国栋. 结合新闻和评论文本的读者情绪分类方法[J]. 山东大学学报（理学版）, 2018, 53(9): 35-39.
[4] 原伟,唐亮,易绵竹. 基于本体的俄文新闻话题检测设计与实现[J]. 山东大学学报（理学版）, 2018, 53(9): 49-54.
[5] 廖祥文,张凌鹰,魏晶晶,桂林,程学旗,陈国龙. 融合时间特征的社交媒介用户影响力分析[J]. 山东大学学报（理学版）, 2018, 53(3): 1-12.
[6] 余传明,冯博琳,田鑫,安璐. 基于深度表示学习的多语言文本情感分析[J]. 山东大学学报（理学版）, 2018, 53(3): 13-23.
[7] 张军,李竞飞,张瑞,阮兴茂,张烁. 基于网络有效阻抗的社区发现算法[J]. 山东大学学报（理学版）, 2018, 53(3): 24-29.
[8] 庞博,刘远超. 融合pointwise及深度学习方法的篇章排序[J]. 山东大学学报（理学版）, 2018, 53(3): 30-35.
[9] 陈鑫,薛云,卢昕,李万理,赵洪雅,胡晓晖. 基于保序子矩阵和频繁序列模式挖掘的文本情感特征提取方法[J]. 山东大学学报（理学版）, 2018, 53(3): 36-45.
[10] 王彤,马延周,易绵竹. 基于DTW的俄语短指令语音识别[J]. 山东大学学报（理学版）, 2017, 52(11): 29-36.
[11] 张晓东,董唯光,汤旻安,郭俊锋,梁金平. 压缩感知中基于广义Jaccard系数的gOMP重构算法[J]. 山东大学学报（理学版）, 2017, 52(11): 23-28.
[12] 孙建东,顾秀森,李彦,徐蔚然. 基于COAE2016数据集的中文实体关系抽取算法研究[J]. 山东大学学报（理学版）, 2017, 52(9): 7-12.
[13] 王凯,洪宇,邱盈盈,王剑,姚建民,周国栋. 一种查询意图边界检测方法研究[J]. 山东大学学报（理学版）, 2017, 52(9): 13-18.
[14] 杨艳,徐冰,杨沐昀,赵晶晶. 一种基于联合深度学习模型的情感分类方法[J]. 山东大学学报（理学版）, 2017, 52(9): 19-25.
[15] 原伟,易绵竹. 基于维基百科的俄汉可比语料库构建及可比度计算[J]. 山东大学学报（理学版）, 2017, 52(9): 1-6.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
版权所有 © 2018 《山东大学学报(理学版)》编辑部　
地址: 山东省济南市山大南路27号山东大学中心校区（250100）　电话: +86-0531-88366917　E-mail: xblxb@sdu.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发