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Related Search Recommendation with User Feedback Session

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Web Recommendations Systems

Abstract

Keyword-based search is an extensively used method to discover knowledge on the Web. Generally, Web users are unable to arrange and define input queries relevant to their search because of adequate knowledge about the domain. Therefore, the input queries are normally ambiguous and short. Query suggestion is a method to recommend queries related to the user input query that helps them to locate their required information more precisely. It helps the search engine to provide relevant answers and meet users needs. Usually, users query keywords are ambiguous, therefore it is not good to use users query keyword in suggestion. In this chapter, Related Search Recommendation (RSR) framework is presented that determines keywords presented in un-clicked and clicked documents in the feedback session. Feedback sessions are used to retrieve users need in terms of Pseudo documents. Semantic similarity is computed between the terms in the Pseudo document. The semantic terms are used for suggestions. The presented method provides semantically related search queries for the user input query. Results show that the RSR method outperforms Rochios model and Snippet Click Model.

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References

  1. M.P. Kato, S. Tetsuya, T. Katsumi, When do people use query suggestion? A query suggestion log analysis. Journal of Information Retrieval, 16(6), 725–746 December (2013)

    Google Scholar 

  2. S. Craig, M. Hannes, H. Monika, M. Michael, Analysis of a very large web search engine query log, in SIGIR Forum (1999), pp. 6–12

    Google Scholar 

  3. C. Huanhuan, J. Daxin, P. Jian, H. Qi, L. Zhen, C. Enhong, L. Hang, Context-aware query suggestion by mining click-through and session data. KDD ’08, in The Proceedings of 14th International ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2008), pp 875–883

    Google Scholar 

  4. M. Qiaozhu, Z. Dengyong, C. Kenneth, Query suggestion using hitting time, in The Proceedings of 17th ACM Conference on Information and Knowledge Management, CIKM ’08 (2008), pp. 469–477

    Google Scholar 

  5. M. Hao, M.R. Lyu, I. King, Diversifying query suggestion results, in The Proceedings of 24th AAAI International Conference on Artificial Intelligence, AAAI ’10 (2010), pp. 1399–1404

    Google Scholar 

  6. J. Guo, X. Cheng, G. Xu, H. Shen, A structured approach to query recommendation with social annotation data, in The Proceedings of 19th ACM International Conference on Information and Knowledge Management, CKIM ’10 (2010), pp. 619–628

    Google Scholar 

  7. Y. Song, D. Zhou, L.-W. He, Post ranking query suggestion by diversifying search results, in The Proceedings of 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’11 (2011), pp. 815–824

    Google Scholar 

  8. Z. Kunpeng, W. Xiaolong, L. Yuanchao, A new query expansion method based on query logs mining. Int. J. Asian Lang. Process. 19(1), 1–12 (2009)

    Google Scholar 

  9. Y. Chen, Y.-Q. Zhang, A personalized query suggestion agent based on query-concept bipartite graphs and concept relation trees. Int. J. Adv. Intell. Parad. 1(4), 398–417 (2009)

    Google Scholar 

  10. Z. Lu, H. Zha, X. Yang, W. Lin, Z. Zheng, A new algorithm for inferring user search goals with feedback sessions. IEEE Trans. Knowl. Data Eng. 25(3), 502–513 (2013)

    Article  Google Scholar 

  11. J. Miao, J.X. Huang, Z. Ye, Proximity based Rocchios model for pseudo relevance feedback, in The Proceedings of 35th ACM International Conference on Research and Development in Information Retrieval, SIGIR’ 12 (2012), pp. 535–544

    Google Scholar 

  12. M.S. Hamani, R. Maamri, Word semantic similarity based on document’s title, in The Proceedings of 24th IEEE International Workshop on Database and Expert Systems Applications (2013), pp. 43–47

    Google Scholar 

  13. D. Bollegala, Y. Matsuo, M. Ishizuka, Measuring semantic similarity between words using web search engines, in The Proceedings of 16th International Conference on World Wide Web, WWW ’07 (2007), pp. 757–766

    Google Scholar 

  14. P. Li, H. Wang, K.Q. Zhu, Z. Wang, X. Wu, Computing term similarity by large probabilistic \(isA\) knowledge, in The Proceedings of 22nd International Conference on Information and Knowledge Management, CIKM ’13 (2013), pp. 1401–1413

    Google Scholar 

  15. B. Danushka, M. Yutaka, I. Mitsuru, A relational model of semantic similarity between words using automatically extracted lexical pattern clusters from the web, in The Proceedings of International Conference on Empirical Methods in Natural Language Processing, EMNLP ’09 (2009), pp. 803–812

    Google Scholar 

  16. B. Danushka, M. Yutaka, I. Mitsuru, A web search engine-based approach to measure semantic similarity between words. IEEE Trans. Knowl. Data Eng. 23(7), 977–990 (2011)

    Article  Google Scholar 

  17. P. Resnik, Using information content to evaluate semantic similarity in a taxonomy, in The Proceedings of the 14th International Joint Conference on Artificial Intelligence (1995), pp. 448–453

    Google Scholar 

  18. D. Lin, An information theoretic definition of similarity, in The Proceedings of the Fifteenth International Conference on Machine Learning (1998), pp. 296–304

    Google Scholar 

  19. C. Leacock, M. Chodorow, Combining local context and wordnet similarity for word sense disambiguation, in WordNet: An Electronics Lexical Database (1998), pp. 265–283

    Google Scholar 

  20. J.J. Jiang, D.W. Conrath, Semantic similarity based on corpus statistics and lexical taxonomy, in The Proceedings of International Conference on Research in Computational Linguistics (1997), pp. 19–33

    Google Scholar 

  21. E. Agirre, E. Alfonseca, K. Hall, J. Kravalova, M. Paşca, A. Soroa, A study on similarity and relatedness using distributional and wordnet-based approaches, in The Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics (2009), pp. 19–27

    Google Scholar 

  22. G. Hirst, D. St-Onge, Lexical chains as representations of context for the detection and correction of malapropisms, in WordNet: An Electronics Lexical Database (1998), pp. 305–332

    Google Scholar 

  23. Y. Song, L.-W. He, Optimal rare query suggestion with implicit user feedback, in The Proceedings of 19th International Conference on World Wide Web, WWW ’10, pp. 901–910 (2010)

    Google Scholar 

  24. N. Craswell, M. Szummer, Random walks on the click graph, in The Proceedings of 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’07 (2007), pp. 239–246

    Google Scholar 

  25. F. Radlinski, T. Joachims, Query chains: learning to rank from implicit feedback, in The Proceedings of 11th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, KDD ’05 (2005), pp. 239–248

    Google Scholar 

  26. G.-R. Xue, H.-J. Zeng, Z. Chen, Y. Yu, W.-Y. Ma, W. Xi, W. Fan, Optimizing web search using web click through data, in The Proceedings of 13th ACM International Conference on Information and Knowledge Management, CIKM ’04 (2004), pp. 118–126

    Google Scholar 

  27. E. Kharitonov, C. Macdonald, P. Serdyukov, L. Ounic, Intent estions, in The Proceedings of 22nd ACM Conference on Information and Knowledge Management, CIKM ’13 (2013), pp. 2303–2308

    Google Scholar 

  28. U. Ozertem, O. Chapelle, P. Donmez, E. Velipasaoglu, Learning to suggest: a machine learning framework for ranking query suggestions, in The Proceeding of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’12 (2012), pp. 25–34

    Google Scholar 

  29. D. Broccolo, L. Marcon, F.M. Nardini, R. Perego, S. Fabrizio, Generating suggestions for queries in the long tail with an inverted index. Int. J. Inf. Process. Manag. 48(2) (2012)

    Google Scholar 

  30. X. Zhang, S. Zilles, R.C. Holte, Improved query suggestion by query search, in The Proceedings of the 35th Annual German Conference on Artificial Intelligence (2012), pp. 205–216

    Google Scholar 

  31. E. Gomez-Nieto, R.F. San, P. Pagliosa, W. Casaca, E.S. Helou, M.C.F. de Oliveira, L.G. Nonato, Similarity preserving snippet-based visualization of web search results. IEEE Trans. Vis. Comput. Graph. 20(3), 457–470 (2014)

    Article  Google Scholar 

  32. X.-H. Phan, C.-T. Nguyen, D.-T. Le, L.-M. Nguyen, S. Horiguchi, Q.-T. Ha, A hidden topic-based framework toward building applications with short web documents. IEEE Trans. Knowl. Data Eng. 23(7), 961–976 (2011)

    Article  Google Scholar 

  33. Q. He, D. Jiang, Z. Liao, S.C.H. Hoi, K. Chang, E.-P. Lim, H. Li, Web query recommendation via Sequential Query Prediction, in The Proceedings of IEEE 25th International Conference on Data Engineering (2009), pp. 1443–1454

    Google Scholar 

  34. Q. Jiang, M. Sun, Fast query recommendation by search, in AAAI Conference on Artificial Intelligence (2011), pp. 1192–1197

    Google Scholar 

  35. L. Li, G. Xu, Z. Yang, P. Dolog, Y. Zhang, M. Kitsuregawa, An efficient approach to suggesting topically related web queries using hidden topic model. Int. J. World Wide Web 16(3), 273–297 (2013)

    Article  Google Scholar 

  36. Y. Liu, J. Miao, M. Zhang, S. Ma, L. Ru, How do users describe their information need: query recommendation based on snippet click model. Int. J. Expert. Syst. Appl. 38(11), 13874–13856 (2011)

    Google Scholar 

  37. J.J. Rocchio, Relevance feedback in information retrieval, in The SMART Retrieval System: Experiments in Automatic Document, ed. by G. Salton (1971), pp. 313–323

    Google Scholar 

  38. C. Fellbaum, WordNet: An Electronic Lexical Database. Language, Speech, and Communication. MIT Press, Cambridge (1998)

    Google Scholar 

  39. R. Jones, K.L. Klinkner, Beyond the session timeout: automatic hierarchical segmentation of search topics in query logs, in ACM Conference on Information and Knowledge Management (2008), pp. 699–708

    Google Scholar 

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Author information

Authors and Affiliations

  1. Bangalore University, Jnana Bharathi, Bengaluru, 560056, India

    K. R. Venugopal

  2. BNM Institute of Technology, Banashankari, Bengaluru, 560070, India

    Sejal Santosh Nimbhorkar

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  1. K. R. Venugopal
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  2. Sejal Santosh Nimbhorkar
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Correspondence to K. R. Venugopal .

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Venugopal, K.R., Santosh Nimbhorkar, S. (2020). Related Search Recommendation with User Feedback Session. In: Web Recommendations Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-2513-1_6

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  • DOI: https://doi.org/10.1007/978-981-15-2513-1_6

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-2512-4

  • Online ISBN: 978-981-15-2513-1

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