A Web Search Personalization Based on Probability of Semantic Similarity between User Log and Query with Web Page

 
 
 
  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract


    Web search personalization is recognized as a competent solution to address the problem of query-relevant search as per the user interest, while it able to present dissimilar search results based upon the preferences and information requirements of users. The popular search engines provide their search results interpreting the user query only, which mostly have unrelated results due to the keywords ambiguity problem. In order to have satisfied and user interesting result, it is important to personalize the results according to their relevancies. In this paper, we propose a Web search Personalization based on a Probability of Semantic Similarity (WP-PSS) between user log and query with search result webpage. It performs a probability of semantic similarities computation between the user query and search result webpage snippet, and compute the frequency of link associated with the log data. Based on these two computed factors a probability of similarities association is computed to group and re-rank the search results for the personalization. Experiment evaluation over a set of multi-domain web searched data collection shows an accuracy improvisation.


  • References


      [1] Y. Tang, H. Wang, K. Guo, Y. Xiao, T. Chi, "Relevant Feedback Based Accurate and Intelligent Retrieval on Capturing User Intention for Personalized Websites", IEEE Access, Vol. 6, pp. 24239 - 24248, 2018.

      [2] L. Yao, Q. Z. Sheng, Anne. H.H. Ngu, J. Yu, and A. Segev, "Unified Collaborative and Content-Based Web Service Recommendation", IEEE Transactions on Services Computing, Vol. 8(3), 2015.

      [3] S. Hernández, P. Álvarez, J. Fabra, J. Ezpeleta, "Analysis of Users' Behavior in Structured e-Commerce Websites", IEEE Access, Vol. 5, pp. 11941 - 11958, 2017.

      [4] S. M. Al-Ghuribi, S. Alshomrani, "A Comprehensive Survey on Web Content Extraction Algorithms and Techniques", International Conference on Information Science and Applications (ICISA) pp. 1 - 5, 2013.

      [5] P. Das, R. C. Jisha, G. P. Sajeev, "Adaptive web personalization system using splay tree", International Conference on Advances in Computing, Communications and Informatics (ICACCI) pp. 1582 - 1587, 2017.

      [6] J. Liu, N. J. Belkin, "Personalizing information retrieval for multisession tasks: Examining the roles of task stage, task type, and topic knowledge on the interpretation of dwell time as an indicator of document usefulness", Journal of the Association for Information Science and Technology, Vol. 66(1), pp. 58-81, 2015.

      [7] C. Chen, X. Meng, Z. Xu, T. Lukasiewicz, "Location-Aware Personalized News Recommendation With Deep Semantic Analysis", IEEE Access, Vol. 5, pp. 1624 - 1638, 2017.

      [8] Y. Zuo, J. Zeng, M. Gong, and L. Jiao, "Tag-aware recommender systems based on deep neural networks", Journal of Neurocomputing, vol. 204, pp. 51 - 60, Sep. 2016.

      [9] T. T. Sang Nguyen, H. Y. Lu, and J. Lu, "Web-Page Recommendation Based on Web Usage and Domain Knowledge", IEEE Transactions On Knowledge And Data Engineering, Vol. 26(10), Oct. 2014.

      [10] R. Li, Z. Xu, W. Kang, K. C. Yow, and C.-Z. Xu, "Efficient multi-keyword ranked query over encrypted data in cloud computing", Future Generation, Computer System, Vol. 30, no. 1, pp. 179 - 190, 2014.

      [11] Y. Bengio, A. Courville, and P. Vincent, "Representation learning: A review and new perspectives", IEEE Trans. Pattern Anal. Machine Intelligent, Vol. 35(8), pp. 1798 - 1828, 2013.

      [12] A. Van den Oord, S. Dieleman, and B. Schrauwen, "Deep content-based music recommendation", In Proc. Adv. Neural Inf. Process. System, pp. 2643 - 2651, 2013.

      [13] G. Venkataraman, A. Ravichandran, "Adaptive Semantic Search: Re-Ranking of Search Results Based on Web page Feature Extraction and Implicitly Learned Knowledge of User Interests", 10th International Conference on Semantics, Knowledge and Grids Pp: 75 - 78, 2014.

      [14] T. Moschopoulos, E. Iosif, L. Demetropoulou, A. Potamianos, S. S. Narayanan, "Toward the Automatic Extraction of Policy Networks Using Web Links and Documents", IEEE Transactions on Knowledge and Data Engineering, Vol. 25(10), Pp. 2404 - 2417, 2013.

      [15] E. Gabrilovich and S. Markovitch, "Wikipedia-based semantic interpretation for natural language processing", Journal of Artificial Intell. and Reseach, vol. 34(1), pp. 443 - 498, 2009.

      [16] S.-X. Bing and Fu-Lei, "Keyword Extraction Algorithm Based on", International Conference on Computational and Information Sciences, Pp. 664 - 665, 2013.

      [17] Y. Wu, L. Shou, T. Hu, and G. Chen, "Query triggered crawling strategy: Build a time-sensitive vertical search engine", In Proc. IEEE Int. Conf. Cyberworlds, pp. 422 - 427, 2008.

      [18] Z. Dou, R. Song, and J. Wen, "A large-scale evaluation and analysis of personalized search strategies", In Proc. 33rd Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, 2007.

      [19] G. Jeh and J. Widom, "Scaling personalized web search", In Proceedings of the Twelfth Intl. World Wide Web Conf., 2003.

      [20] P. A. Chirita, C. Firan and W. Nejdl, "Summarizing local context to personalize global web search", In Proc. Int. Conf. on Information and Knowledge Management, 2006.

      [21] J. Teevan, S. T. Dumais, and E. Horvitz, "Personalizing search via automated analysis of interests and activities", In Proc. 31st Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, pp. 449–456, 2005.

      [22] P. A. Chirita, W. Nejdl, R. Paiu and C. Kohlschutter, "Using ODP metadata to personalize search", In Proc. 31st Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, pp. 178–185, 2005.

      [23] F. Qiu and J. Cho, "Automatic identification of user interest for the personalized search", In Proc. 15th Int. World Wide Web Conference, pp. 727–736, 2006.

      [24] A. T. A. Thuy Vu and M. Zhang, "Term extraction through the unit hood and termhood unification", In Proc. of Int'l Joint Conf on Natural Language Proc., 2008.

      [25] S. Ganesh, M. Jayaraj, V. Kalyan, and G. Aghila, "Ontology-based web crawler", In Proc. ITCC: Coding Computing, Las Vegas, NV, pp. 337–341, 2004.

      [26] R. Krovetz and W. B. Croft, "Lexical ambiguity and information retrieval", Information Systems, Vol. 10(2), pp. 115–141, 1992.

      [27] X. Shen, B. Tan and C. Zhai "Implicit user modeling for the personalized search", In Proc. Int. Conf. on Information and Knowledge Management, pp. 824–831, 2005.

      [28] T.Haveliwala,"Topic-Sensitive PageRank", In Proceedings of the Eleventh Intl. World Wide Web Conf., 2002.

      [29] F. Liu, C. Yu, and W. Meng, "Personalized web search by mapping user query to categories", In Proc. Int. Conf. on Information and Knowledge Management, 2002, pp. 558–565, 2002.

      [30] B. J. Jansen, A. Spink, and T Saracevic, "Real life, real users, and real needs: A study and analysis of user query on the Web", Information Processing and Management, Vol. 36(2), pp. 207 – 227, 2000.


 

View

Download

Article ID: 21856
 
DOI: 10.14419/ijet.v7i4.24.21856




Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.