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Art. 05 – Vol. 25 – No. 1 – 2015

Personalized Search System for Re-Ranking Web Search Results

Ciprian CÂNDEA
ciprian.candea@ropardo.ro

Ropardo SRL, Sibiu – Department for Research and Development

Abstract: This paper presents a personalized search system for re-ranking web search results. The system is accessible from the web browser. By rating web pages, the user may create multiple profiles that can be used to personalize the search results provided by the search engine. A User Profile algorithm is used to learn the user’s preferences using a hierarchical weighted keywords model. The potential of such personalized system is evaluated through experiments. Furthermore, two Information Retrieval algorithms, LSI and FCRN, are implemented in order to evaluate potential improvements of the retrieval process, using data from the User Profile. A hybrid algorithm, between LSI and FCRN, is also proposed and evaluated. Through experiments, which use the performance of the search engine as a baseline, the benefits of the personalized search system are presented.

Keywords: Personalized Search, User Profile, Information Retrieval, Similarity Metrics, Singular Value Decomposition, Evaluation.

REFERENCES

  1. PITKOW, J.; SCHÜTZE, H.; CASS, T.; COOLEY, R.; TURNBULL, D.; EDMONDS, A.; ADAR, E.; BREUEL, T.: Personalized Search. Commun ACM, vol. 45, no. 9, Sep. 2002, pp. 50–55.
  2. SALTON, G.; McGILL, M. J.: Introduction to Modern Information Retrieval. New York, NY, USA: McGraw-Hill, Inc., 1986.
  3. *** “Personalized Search for everyone”, Official Google Blog.
  4. *** “Making search yours | Search Blog” [Online]. Available: http://blogs.bing.com/search/ 2011/02/10/making-search-yours/. [Accessed: 23-Oct-2014].
  5. WRITER, M. H. S.; NEWS, C.: Yahoo debuts personalized search – CNET News. CNET. [Online]. Available: http://news.cnet.com/Yahoo-debuts-personalized-search/2100-1038_3-5686585.html. [Accessed: 23-Oct-2014].
  6. HANNAK, A.; SAPIEZYNSKI, P.; MOLAVI KAKHKI, A.; KRISHNAMURTY, B.; LAZER, D.; MISLOVE, A.; WILSON, C.: Measuring Personalization of Web Search. In Proceedings of the 22Nd International Conference on World Wide Web, Republic and Canton of Geneva, Switzerland, 2013, pp. 527–538.
  7. WIDYANTORO, D. H.: Dynamic Modeling and Learning User Profile in Personalized News Agent. Texas A&M University, 1999.
  8. SUGIYAMA, K.; HATANO, K.; YOSHIKAWA, M.: Adaptive Web Search Based on User Profile Constructed Without Any Effort from Users. In Proceedings of the 13th International Conference on World Wide Web, New York, NY, USA, 2004, pp. 675–684.
  9. TEEVAN, J.; DUMAIS, S. T.; HORVITZ, E.: Personalizing Search via Automated Analysis of Interests and Activities. In Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, New York, NY, USA, 2005, pp. 449–456.
  10. GAUCH, S.; CHAFEE, J.; PRETSCHNER, A.: Ontology-based Personalized Search and Browsing. Web Intelli Agent Sys, vol. 1, no. 3–4, Dec. 2003, pp. 219–234.

View full article

  1. QIU, F.; CHO, J.: Automatic Identification of User Interest for Personalized Search. In Proceedings of the 15th International Conference on World Wide Web, New York, NY, USA, 2006, pp. 727–736.
  2. SUN, J.-T.; ZENG, H.-J.; LIU, H.; LU, Y.; CHEN, Z.: CubeSVD: A Novel Approach to Personalized Web Search. In Proceedings of the 14th International Conference on World Wide Web, New York, NY, USA, 2005, pp. 382–390.
  3. SHEN, X.; TAN, B.; ZHAI, C.: Implicit User Modeling for Personalized Search. In Proceedings of the 14th ACM International Conference on Information and Knowledge Management, New York, NY, USA, 2005, pp. 824–831.
  1. TAN, B.; SHEN, X.; ZHAI, C.: Mining Long-term Search History to Improve Search Accuracy. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA, 2006, pp. 718–723.
  2. .PARISER, E.: The Filter Bubble: How the New Personalized Web Is Changing What We Read and How We Think. Penguin, 2011.KOEHN, P.: Europarl: A Parallel Corpus for Statistical Machine Translation.
  3. *** Coursera – Language Modeling, Coursera. [Online]. Available: https://class.coursera.org/nlp/lecture/17. [Accessed: 16-Oct-2014].
  1. PORTER, M. F.: Readings in Information Retrieval. K. Sparck Jones and P. Willett, Eds. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., 1997, pp. 313–316.
  2. FIELDING, R. T.; TAYLOR, R. N.: Principled Design of the Modern Web Architecture. ACM Trans Internet Technol, vol. 2, no. 2, May 2002, pp. 115–150.
  3. BERRY, M.; DUMAIS, S.; O’BRIEN, G.: Using Linear Algebra for Intelligent Information Retrieval. SIAM Rev., vol. 37, no. 4, Dec. 1995, pp. 573–595.
  4. SALTON, G.: WONG, A.; YANG, C. S.: A Vector Space Model for Automatic Indexing. Commun ACM, vol. 18, no. 11, Nov. 1975, pp. 613–620.
  5. BERRY, M. W.;DRMAČ, Z.; JESSUP, E. and R.: Matrices, vector spaces, and information retrieval. SIAM Rev., vol. 41, 1999, pp. 335–362.
  6. RICCI, F.; ROKACH, L.; SHAPIRA, B.; KANTOR, P. B. Eds.: Recommender Systems Handbook, 2011 edition. New York: Springer, 2010.
  7. LENZ, M.; BURKHARD, H.-D.: Case retrieval nets: Basic ideas and extensions. In KI-96: Advances in Artificial Intelligence, G. Görz and S. Hölldobler, Eds. Springer Berlin Heidelberg, 1996, pp. 227–239.
  8. KONTOSTATHIS, A.; POTTENGER, W. M.: A Mathematical View of Latent Semantic Indexing: Tracing Term Co-Occurrences, 2002.
  9. RIJSBERGEN, C. J. V.: Information Retrieval. 2nd ed. Newton, MA, USA: Butterworth-Heinemann, 1979.
  10. TEUFEL, S.: An Overview of Evaluation Methods in TREC Ad Hoc Information Retrieval and TREC Question Answering. In Evaluation of Text and Speech Systems, P. L. Dybkjær, H. Hemsen, and P. W. Minker, Eds. Springer Netherlands, 2007, pp. 163–186.
  11. LIU, F.: Personalized web search by mapping user queries to categories. 2002, pp. 558–565.

 

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