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Modelling User Behaviour on Page Content and Layout in Recommender Systems

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Book cover Learning Structure and Schemas from Documents

Part of the book series: Studies in Computational Intelligence ((SCI,volume 375))

Abstract

With the exponential growth of information on the Web, recommender systems play an important role in many service applications such as e-commerce and e-learning. Recommender systems are used to assist users in navigating the Web or propose items that the users are likely interested in. Most of the currently prevalent approaches use collaborative filtering based on the preference of a group of similar users. In the past decade, there has been some but rather limited research in personalized recommender systems incorporating an individual user’s explicit and implicit feedbacks. In our previous work, a personalized recommender system that extracts an individual user’s preference and the associated Web browsing behaviour such as print and bookmark, has been designed and implemented. In this chapter, Web browsing behaviour reflecting a user’s preference on layout and design is investigated. We postulate that when a user browses a page, her actions on the content and links could be associated with personal preference on an object’s location, icon shape, colour scheme, etc. Furthermore, tags and labels of selected objects contain valuable information to facilitate the recommendation process. Consequently, systematic and automatic analysis of the relationship between information preference and Web browsing behaviour based on structure and schema learning could be exploited to complement recommendation utilizing content similarity. Survey and related work on personal recommender systems that model Web browsing behaviour are presented. A proof-of-concept system is designed with the objective to study whether there is a correlation between browsing behaviour, both in the content and visual aspects of a Web page, and user preference.

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References

  1. BBC, http://www.bbc.co.uk/

  2. Bilenko, M., White, R.W.: Mining the Search Trails of Surfing Crowds: Identifying Relevant Websites from User Activity. In: Proceedings of the 17th International World Wide Web Conference, pp. 51–60 (2008)

    Google Scholar 

  3. Brafman, R.I., Domshlak, C., Shimony, S.E.: Qualitative Decision Making in Adaptive Presentation of Structured Information. ACM Transactions on Information Systems TOIS Homepage archive 22(4) (2004)

    Google Scholar 

  4. Breese, J.S., Heckerman, D., Kadie, C.: Empirical Analysis of Predictive Algorithms for Collaborative Filtering. In: Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, pp. 43–52 (1998)

    Google Scholar 

  5. Burklen, S., Marron, P.J., Fritsch, S., Rothermel, K.: User Centric Walk: An Integrated Approach for Modeling the Browsing Behavior of Users on the Web. In: Proceedings of the 38th Annual Symposium on Simulation, pp. 149–159 (2005)

    Google Scholar 

  6. Cai, D., He, X., Wen, J.-R., Ma, W.-Y.: Block-level Link Analysis. In: Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 440–447 (2004)

    Google Scholar 

  7. Chen, Y., Ma, W.-Y., Zhang, H.-J.: Detecting Web Page Structure for Adaptive Viewing on Small Form Factor Devices. In: Proceedings of the 12th International Conference on World Wide Web, pp. 225–233 (2003)

    Google Scholar 

  8. Chirita, P.-A., Firan, C.S., Nejdl, W.: Personalized Query Expansion for the Web. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 7–14 (2007)

    Google Scholar 

  9. Claypool, M., Brown, D., Le, P., Waseda, M.: Inferring User Interest. IEEE Internet Computing, 32–39 (November/December 2001)

    Google Scholar 

  10. Dumais, S., Cutrell, E., Cadiz, J.J., Jancke, G., Sarin, R., Robbins, D.C.: Stuff I’ve Seen: A System for Personal Information Retrieval and Re-Use. In: Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 72–79 (2003)

    Google Scholar 

  11. Dupret, G., Piwowarski, B.: A User Browsing Model to Predict Search Engine Click Data from Past Observations. In: Proceedings of the 31st annual international ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 331–338 (2008)

    Google Scholar 

  12. Fiala, Z., Hinz, M., Houben, G.-J., Frasincar, F.: Design and Implementation of Componentbased Adaptive Web Presentations. In: Proceedings of the ACM Symposium on Applied Computing, pp. 1698–1704 (2004)

    Google Scholar 

  13. Francisco-Revilla, L., Crow, J.: Interpreting the Layout of Web Pages. In: Proceedings of the 20th ACM Conference on Hypertext and Hypermedia, pp. 157–166 (2009)

    Google Scholar 

  14. He, X., Cai, D., Wen, J.-R., Ma, W.-Y., Zhang, H.-J.: Clustering and Searching WWW Images Using Link and Page Layout Analysis. Proceedings of the ACM Transactions on Multimedia Computing, Communications, and Applications 3(2) (2007)

    Google Scholar 

  15. Huang, J., White, R.W.: Parallel Browsing Behavior on the Web. In: Proceedings of the 21st ACM Conference on Hypertext and Hypermedia, pp. 13–18 (2010)

    Google Scholar 

  16. iGoogle, http://www.google.com/ig

  17. Karreman, J., Loorbach, N.: Paragraphs or Lists? The Effects of Text Structure on Web Sites. In: Proceedings of the IEEE International Professional Communication Conference IPCC, pp. 1–5 (2007)

    Google Scholar 

  18. Kawai, D., Kanjo, D., Tanaka, K.: My Portal Viewer for Content Fusion Based on User’s Preferences. In: Proceedings of the IEEE International Conference on Multimedia and Expo., pp. 2163–2166 (2004)

    Google Scholar 

  19. Kelly, D., Teevan, J.: Implicit Feedback for Inferring User Preference: A Bibliography. ACM SIGIR Forem 37(2), 18–28 (2003)

    Article  Google Scholar 

  20. Kumar, R., Tomkins, A.: Characterization of Online Browsing Behavior. In: Proceedings of the 19th International Conference on World Wide Web, pp. 561–570 (2010)

    Google Scholar 

  21. Lam, W.W.M., Chan, K.: Analyzing Web Layout Structures Using Graph Mining. In: IEEE International Conference on Granular Computing, pp. 361–366 (2008)

    Google Scholar 

  22. Lee, J., Choi, G., Jang, J., Nang, J.: An Effective Keyword Extraction Method for Videos in Web pages by Analyzing their Layout Structures. In: Proceedings of the IEEE Region 10 Conference TENCON, pp. 1–4 (2007)

    Google Scholar 

  23. Lerman, K., Getoor, L., Minton, S., Knoblock, C.: Using the Structure of Web Sites for Automatic Segmentation of Tables. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 119–130 (2004)

    Google Scholar 

  24. Li, Y., Feng, B.-Q.: Page Interest Estimation Model Considering User Interest Drift. In: Proceedings of the 4th International Conference on Computer Science & Education, pp. 1893–1896 (2009)

    Google Scholar 

  25. Liang, T.-P., Lai, H.-J.: Discovering User Interests from Web Browsing Behavior: An Application to Internet News Services. In: Proceedings of the 35th Annual Hawaii International Conference on System Sciences, pp. 2718–2727 (2002)

    Google Scholar 

  26. Lim, S.H., Zheng, L., Jin, J., Hou, H., Fan, J., Liu, J.: Automatic Selection of Print-worthy Content for Enhanced Web Page Printing Experience. In: Proceedings of the 10th ACM Symposium on Document Engineering, pp. 165–168 (2010)

    Google Scholar 

  27. Liu, C., White, R.W., Dumais, S.: Understanding Web Browsing Behaviors Through Weibull Analysis of Dwell Time. In: Proceeding of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 379–386 (2010)

    Google Scholar 

  28. Marcialis, I., Vita, E.D.: SEARCHY: An Agent to Personalize Search Results. In: Proceedings of the 3rd International Conference on Internet and Web Applications and Services, pp. 512–517 (2008)

    Google Scholar 

  29. Morita, T., Hidaka, T., Tanaka, A., Kato, Y.: System for Reminding a User of Information Obtained Through a Web Browsing Experience. In: Proceedings of the 16th International World Wide Web Conference, pp. 1327–1328 (2007)

    Google Scholar 

  30. Morita, M., Shinoda, Y.: Information Filtering Based on User Behavior Analysis and Best Match Text Retrieval. In: Proceeding of the 17th ACM SIGIR, pp. 272–281 (1994)

    Google Scholar 

  31. MyYahoo!, http://my.yahoo.com/

  32. Oard, D.W., Kim, J.: Modeling Information Content Using Observable Behavior. In: Proceedings of the ASIST Annual Meeting, vol. 38, pp. 481–488 (2001)

    Google Scholar 

  33. Resnick, P., Iacovou, N., Sushak, M., Bergstrom, P., Reidl, J.: GroupLens: An Open Architecture for Collaborative Filtering of Netnews. In: Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work Conference, pp. 175–186 (1994)

    Google Scholar 

  34. Sah, M., Hall, W., De Roure, D.C.: Dynamic Linking and Personalization on Web. In: Proceedings of the ACM Symposium on Applied Computing, pp. 1404–1410 (2010)

    Google Scholar 

  35. Sarwar, B.M., Karypis, G., Konstan, J.A., Riedl, J.: Analysis of Recommendation Algorithms for E-Commerce. In: Proceedings of ACM Conference on Electronic Commerce, pp. 158–167 (2000)

    Google Scholar 

  36. Seo, Y.-W., Zhang, B.-T.: Learning User’s Preferences by Analyzing Web-Browsing Behaviors. In: Proceedings of the 4th International Conference on Autonomous Agents, pp. 381–387 (2000)

    Google Scholar 

  37. Song, R., Liu, H., Wen, J.-R., Ma, W.-Y.: Learning Important Models for Web Page Blocks Based on Layout and Content Analysis. Proceedings of the SIGKDD Explorations Newsletter 6(2), 14–23 (2004)

    Article  Google Scholar 

  38. Spalteholz, L., Li, K.F., Livingston, N.: KeySurf: A Character Controlled Browser for People with Physical Disabilities. In: Proceedings of the 17th International World Wide Web Conference, pp. 31–39 (2008)

    Google Scholar 

  39. Takano, K., Li, K.F.: An Adaptive Personalized Recommender Based on Web-Browsing Behaviour Learning. In: Proceedings of the 2009 IEEE International Symposium on Mining and Web, pp. 654–660 (2009)

    Google Scholar 

  40. Teevan, J., Dumais, S.T., Horvitz, E.: Potential for Personalization. Proceeding of the ACM Transactions on Computer-Human Interaction 17(1) (2010)

    Google Scholar 

  41. Tso-Sutter, K.H.L., Marinho, L.B., Schmidt-Thieme, L.: Tag-aware Recommender Systems by Fusion of Collaborative Filtering Algorithms. In: Proceedings of the 2008 ACM Symposium on Applied Computing, pp. 1995–1999 (2008)

    Google Scholar 

  42. Weinreich, H., Obendorf, H., Herder, E., Mayer, M.: Not Quite the Average: An Empirical Study of Web Use. Proceedings of the ACM Transactions on the Web 2(1) (2008)

    Google Scholar 

  43. Viermetz, M., Stolz, C., Gedov, V., Skubacz, M.: Relevance and Impact of Tabbed Browsing Behavior on Web Usage Mining. In: Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence, pp. 262–269 (2006)

    Google Scholar 

  44. Yu, X., Liu, F.: A Short-term User Interest Model for Personalized Recommendation. In: Proceedings of the 2nd IEEE International Conference on Information Management and Engineering, pp. 219–222 (2010)

    Google Scholar 

  45. Zheng, L., Cui, S., Yue, D., Zhao, X.: User Interest Modeling based on Browsing Behavior. In: Proceedings of the 3rd International Conference on Advanced Computer Theory and Engineering, pp. V5455–V5458 (2010)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Victoria, Canada

    Kin Fun Li

  2. Department of Information and Computer Sciences, Kanagawa Institute of Technology, Japan

    Kosuke Takano

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  1. Kin Fun Li
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  2. Kosuke Takano
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Editor information

Editors and Affiliations

  1. University of New York Tirana, Rr. Komuna E Parisit,, Tirana, Albania

    Marenglen Biba

  2. Technical University of Catalonia, Campus Nord, Ed. Omega, C/Jordi Girona 1-3, 08034, Barcelona, Spain

    Fatos Xhafa

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Li, K.F., Takano, K. (2011). Modelling User Behaviour on Page Content and Layout in Recommender Systems. In: Biba, M., Xhafa, F. (eds) Learning Structure and Schemas from Documents. Studies in Computational Intelligence, vol 375. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22913-8_14

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  • DOI: https://doi.org/10.1007/978-3-642-22913-8_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-22912-1

  • Online ISBN: 978-3-642-22913-8

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