利用嵌入方法实现个性化查询重构

doi:10.3772/j.issn.1000-0135.2018.06.007

情报学报

2018, Vol. 37

Issue (6): 621-630 DOI: 10.3772/j.issn.1000-0135.2018.06.007

情报分析方法与技术

本期目录 | 过刊浏览 | 高级检索

利用嵌入方法实现个性化查询重构

张晓娟

西南大学计算机与信息科学学院,重庆 400715

Personalized Query Reformulations with Embeddings

Zhang Xiaojuan

College of Computer and Information Science, Southwest University, Chongqing 400715

摘要
图/表
参考文献
相关文章 (5)

全文: PDF (375 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要作为能引导用户表达信息需求的机制,查询重构主要基于用户所提交的历史查询来生成相关候选查询重构列表。为了使得候选查询能与用户最初意图保持一致,当前大多数的查询重构方法方法是根据查询词之间的共现信息来获得查询词的上下文信息,再利用上下文的相似性来生成候选查询推荐,最后通过对查询中词之间的语义一致性建模来对实现候选查询进行排序。而本文中以利用嵌入方法来实现个性化查询重构,即首先利用查询词嵌入技术为每个查询获得该词上下文信息的词向量,再利用词向量进一步构建表征用户偏好的向量,从而基于词向量与用户向量实现根据用户偏好生成候选查询;本文进一步采用主题嵌入来抽取每个潜在主题的上下文信息,最后利用隐马尔可夫模型（HMM）融合词向量、用户向量和主题向量来实现根据用户偏好对候选查询的排序。实验结果表明,本文的方法优于已有相关方法。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	张晓娟

关键词 ：查询重构, 个性化, 词嵌入

收稿日期: 2017-06-19

基金资助:国家社会科学基金青年项目“融合用户个性化与实时性意图的查询推荐模型研究”（15CTQ019）

作者简介: 张晓娟,女,1985年生,博士,副教授,硕士生导师,研究方向为信息检索,E-mail: zxj0614@swu.edu.cn。

引用本文:

张晓娟. 利用嵌入方法实现个性化查询重构[J]. 情报学报, 2018, 37(6): 621-630.
Zhang Xiaojuan. Personalized Query Reformulations with Embeddings. 情报学报, 2018, 37(6): 621-630.

链接本文:

https://qbxb.istic.ac.cn/CN/10.3772/j.issn.1000-0135.2018.06.007 或 https://qbxb.istic.ac.cn/CN/Y2018/V37/I6/621

[1] Whiting S, Jose J M.Recent and robust query auto-completion[C]// Proceedings of the 23rd International Conference on World Wide Web. New York: ACM Press, 2014: 971-982.
[2] Song Y, Zhou D Y, He L W.Query suggestion by constructing term-transition graphs[C]// Proceedings of the Fifth ACM International Conference on Web Search and Data Mining. New York: ACM Press, 2012: 353-362.
[3] Efthimiadis E N.Query expansion[J]. Annual Review of Information Science and Technology, 1996, 31(8): 121-187.
[4] Huang C K, Chien L F, Oyang Y J.Relevant term suggestion in interactive web search based on contextual information in query session logs[J]. Journal of the American Society for Information Science and Technology, 2003, 54(7): 638-649.
[5] Jones R, Rey B, Madani O, et al.Generating query substitutions[C]// Proceedings of the 15th International Conference on World Wide Web. New York: ACM Press, 2006: 387-396.
[6] Fonseca B M, Golgher P B, de Moura E S, et al. Using association rules to discovery search engines related queries[C]// Proceedings of the 1st Conference on Latin American Web Congress. Washington, DC: IEEE Computer Society, 2003: 66.
[7] Shi X D, Yang C C.Mining related queries from search engine query logs[C]// Proceedings of the 15th international Conference on World Wide Web. New York: ACM Press, 2006: 943-944.
[8] Jensen E C, Beitzel S M, Chowdhury A, et al.Query phrase suggestion from topically tagged session logs[C]// Proceedings of the 7th International Conference on Flexible Query Answering Systems. Heidelberg: Springer-Verlag Berlin, 2006: 185-196.
[9] Zhang Z Y, Nasraoui O.Mining search engine query logs for query recommendation[C]// Proceedings of the 15th International Conference on World Wide Web. New York: ACM Press, 2006: 1039-1040.
[10] Zeng Q T, Crowell J, Plovnick R M, et al.Assisting consumer health information retrieval with query recommendations[J]. Journal of the American Medical Informatics Association, 2006, 13(1): 80-90.
[11] 李亚楠, 王斌, 李锦涛. 搜索引擎查询推荐技术综述[J]. 中文信息学报, 2010 , 24(6): 75-84.
[12] Baeza-Yates R, Hurtadoa C, Mendoza M.Query recommendation using query logs in search engines[C]// Proceedings of the 2004 International Conference on Current Trends in Database Technology. Heidelberg: Springer-Verlag Berlin, 2004, 3268: 588-596.
[13] Cao H H, Jiang D X, Pei J, et al.Context-aware query suggestion by mining click-through and session data[C]// Proceedings of 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM Press, 2008: 875-883.
[14] Boldi P, Bonchi F, Castillo C, et al.Query suggestions using query-flow graphs[C]// Proceedings of the 2009 Workshop on Web Search Click Data. New York: ACM Press, 2009: 56-63.
[15] Albakour M D, Kruschwitz U, Adeyanju I, et al.Enriching query flow graphs with click information[C]// Proceedings of the 7th Asia Conference on Information Retrieval Technology. Heidelberg: Springer-Verlag Berlin, 2011: 193-204.
[16] Mei Q Z, Zhou D Y, Church K.Query suggestion using hitting time[C]// Proceedings of the 17th ACM Conference on Information and Knowledge Management. New York: ACM Press, 2008: 469-478.
[17] Song Y, Zhou D Y, He L W.Query suggestion by constructing term-transition graphs[C]// Proceedings of the Fifth ACM International Conference on Web Search and Data Mining. New York: ACM Press, 2012: 353-362.
[18] Sadikov E, Madhavan J, Wang L, et al.Clustering query refinements by user intent[C]// Proceedings of the 19th International Conference on World Wide Web. New York: ACM Press, 2010: 841-850.
[19] Jiang J Y, Ke Y Y, Chien P Y, et al.Learning user reformulation behavior for query auto-completion[C]// Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval. New York: ACM Press, 2014: 445-454.
[20] Shokouhi M.Learning to personalize query auto-completion[C]// Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM Press, 2013:103-112.
[21] Ozertem U, Chapelle O, Donmez P, et al.Learning to suggest: a machine learning framework for ranking query suggestions[C]// Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM Preee, 2012: 25-34.
[22] Sordoni A, Bengio Y, Vahabi H, et al.A hierarchical recurrent encoder-decoder for generative context-aware query suggestion[C]// Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. New York: ACM Press, 2015: 553-562.
[23] Wang X H, Zhai C X.Mining term association patterns from search logs for effective query reformulation[C]// Proceedings of the 17th ACM Conference on Information and Knowledge Management. New York: ACM Press, 2008: 479-488.
[24] Bing L D, Lam W.Investigation of web query refinement via topic analysis and learning with Personalization[C]// Proceedings of SIGIR Workshop on Query Representation and Understanding, Beijing, China, 2011.
[25] Mikolov T, Chen K, Corrado G, et al. Efficient estimation of word representations in vector space[OL]. arXiv preprint arXiv:1301.3781, 2013.
[26] Mikolov T, Sutskever I, Chen K, et al.Distributed representations of words and phrases and their compositionality[C]// Proceedings of the 26th International Conference on Neural Information Processing Systems. Red Hook: Curran Associates, 2013: 3111-3119.
[27] Bordes A, Usunier N, Garcia-Durán A, et al.Translating embeddings for modeling multi-relational data[C]// Proceedings of International Conference on Neural Information Processing Systems. New York: Curran Associates, 2013: 2787-2795.
[28] Kiros R, Salakhutdinov R, Zemel R.Multimodal neural language models[C]// Proceedings of the 31st International Conference on International Conference on Machine Learning. JMLR, 2014: 595-603.
[29] Van Gysel C, de Rijke M, Worring M. Unsupervised, efficient and semantic expertise retrieval[C]// Proceedings of the 25th International Conference on World Wide Web, Montréal, Québec, Canada, 2016: 1069-1079.
[30] Klein B, Lev G, Sadeh G, et al.Associating neural word embeddings with deep image representations using fisher vectors[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2015: 4437-4446.
[31] Grbovic M, Djuric N, Radosavljevic V, et al.Context- and content-aware embeddings for query rewriting in sponsored search[C]// Procedigns of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM Press, 2015: 383-392.
[32] Diaz F, Mitra B, Craswell N.Query expansion with locally- trained word embeddings[C]// Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2016: 367-377.
[33] Zamani H, Croft W B.Estimating embedding vectors for queries[C]// Proceedings of the 2016 ACM International Conference on the Theory of Information Retrieval. New York: ACM Press, 2016: 123-132.
[34] He D Q, Göker A.Detecting session boundaries from Web user logs[C]// Proceedings of the BCS-IRSG 22nd Annual Colloquium on Information Retrieval Research, 2000: 57-66.
[35] Liu Y, Liu Z Y, Chua T S, et al.Topical word embeddings[C]// Proceedings of Twenty-Ninth AAAI Conference on Artificial Intelligence. Palo Alto: AAAI Press, 2015: 2418-2424.