Improving Linkage of Web Pages

Organizations maintain informational websites for wired devices. The information content of such websites tends to change slowly with time, so a steady pattern of usage is soon established. User preferences, both at the individual and at the aggregate level, can then be gauged from user access log files. We propose a heuristic scheme based on simulated annealing that makes use of the aggregate user preference data to re-link the pages to improve navigability. This scheme is also applicable to the initial design of websites for wireless devices. Using the aggregate user preference data obtained from a parallel wired website, and given an upper bound on the number of links per page, our methodology links the pages in the wireless website in a manner that is likely to enable the “typical” wireless user to navigate the site efficiently. Later, when a log file for the wireless website becomes available, the same approach can be used to refine the design further.

[1]  Jonathan Trevor,et al.  m-links: An infrastructure for very small internet devices , 2001, MobiCom '01.

[2]  Tao Luo,et al.  Discovery and Evaluation of Aggregate Usage Profiles for Web Personalization , 2004, Data Mining and Knowledge Discovery.

[3]  Yongcheng Li,et al.  Transcoding: Extending e-business to new environments , 2001, IBM Syst. J..

[4]  Louis B. Rosenfeld,et al.  Web Style Guide: Basic Design Principles for Creating Web Sites , 1999 .

[5]  Sumit Sarkar,et al.  The Role of the Management Sciences in Research on Personalization , 2003, Manag. Sci..

[6]  Sourav S. Bhowmick,et al.  A survey of Web metrics , 2002, CSUR.

[7]  MAGDALINI EIRINAKI,et al.  Web mining for web personalization , 2003, TOIT.

[8]  M. Locatelli.  Simulated Annealing Algorithms for Continuous Global Optimization , 2002 .

[9]  Oren Etzioni,et al.  Adaptive Web sites , 2000, CACM.

[10]  Johan Bollen,et al.  Dynamic Linking of Smart Digital Objects Based on User Navigation Patterns , 2004, ArXiv.

[11]  Clifford Stein,et al.  Introduction to Algorithms, 2nd edition. , 2001 .

[12]  Bamshad Mobasher,et al.  A Hybrid Web Personalization Model Based on Site Connectivity , 2003 .

[13]  Myra Spiliopoulou,et al.  A Framework for the Evaluation of Session Reconstruction Heuristics in Web-Usage Analysis , 2003, INFORMS J. Comput..

[14]  Jesus Mena.  WebMining for Profit: E-Business Optimization , 2001 .

[15]  Umeshwar Dayal,et al.  From User Access Patterns to Dynamic Hypertext Linking , 1996, Comput. Networks.

[16]  Jaideep Srivastava,et al.  Data Preparation for Mining World Wide Web Browsing Patterns , 1999, Knowledge and Information Systems.

[17]  Johan Bollen,et al.  A system to restructure hypertext networks into valid user models , 1998, New Rev. Hypermedia Multim..

[18]  Matthias Baumgarten,et al.  User-Driven Navigation Pattern Discovery from Internet Data , 1999, WEBKDD.

[19]  Thomas H. Cormen,et al.  Introduction to algorithms [2nd ed.] , 2001 .

[20]  Naren Ramakrishnan,et al.  Personalizing Web sites with mixed-initiative interaction , 2003 .

[21]  Sansanee Auephanwiriyakul,et al.  An active transcoding proxy to support mobile web access , 1998, Proceedings Seventeenth IEEE Symposium on Reliable Distributed Systems (Cat. No.98CB36281).

[22]  E. Lawler.  Sequencing Jobs to Minimize Total Weighted Completion Time Subject to Precedence Constraints , 1978 .

[23]  Bruce E. Hajek,et al.  Cooling Schedules for Optimal Annealing , 1988, Math. Oper. Res..

[24]  Virpi Roto,et al.  XHTML in Mobile Application Development , 2002, Mobile HCI.

[25]  John D. Villasenor,et al.  Bringing the Wireless Internet to Mobile Devices , 2001, Computer.