|
|
|
| Personalized Query Reformulations with Embeddings |
| Zhang Xiaojuan |
| College of Computer and Information Science, Southwest University, Chongqing 400715 |
|
|
|
|
Abstract As a mechanism to guide users towards a better representation of their information need, a query reformulation method generates new queries based on those issued by users. To preserve the original search intent, most current query reformulation methods aim to obtain the context information of the query term based on the co-occurrence information between the query terms, and then use the contextual similarities to generate candidate query reformulations. These candidate query reformulations are scored eventually according to the semantic consistency of terms, dependency among latent semantic topics, and users’ preferences. However, we exploit the embeddings method to realize personalized query reformulation. First, we use the query term embedding technique to obtain the vector of each, and this vector represents the contextual information for each term. Second, the vector that characterizes users’ preferences is constructed by using the term vectors, and candidate query reformulations are generated according to user preference based on term vectors and user vectors. Finally, topic embeddings are proposed to extract the context information of each term’s latent topic, and the hidden Markov model (HMM) is used to integrate term vectors, user vectors, and topic vectors to re-rank the candidate query reformulations based on users’ personalization. The final experimental results show that this method outperforms the existing one.
|
|
Received: 19 June 2017
|
|
|
|
[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. |
|
|
|
