Web Page Evaluation based on Implicit User Reactions and Neural Networks

This paper proposes a method for evaluating web pages by considering implicit user reaction on web pages. Usually users spend more time and make more reactions, such as clicking, dragging and scrolling, while reading interesting pages. Based on this observation, a web page evaluation method by observing implicit user reaction is proposed. The system is designed with Ajax for observing user reactions, and neural networks for learning correlation between user reactions and usefulness of pages. The amounts of each type of user reactions are inputted to neural networks. Also the numbers of characters and images of pages are used as inputs because the amount of users’ behaviors has a tendency to increase as the length of pages increase. The experiment is conducted with 113 people and 74 pages. Each page is ranked by users with a questionnaire. The proposed method shows more close ranking results to the user ranks than Google. That is, our system evaluates web pages more closely to users’ viewpoint than Google. Although our experiment is limited, our result shows powerful potential of new element for web page evaluation. Some approaches evaluate web pages with their contents and some evaluate web pages with structural attributes, particularly links, of pages. Web page evaluation is for users, so the best evaluation can be done by users themselves. So, user feedback is one of the most important factors for web page evaluation. This paper proposes a new method which reflects user feedbacks on web pages.

[1]  Fabrizio Silvestri,et al.  Dynamic personalization of web sites without user intervention , 2007, CACM.

[2]  Konstantinos A. Meintanis,et al.  Recognizing user interest and document value from reading and organizing activities in document triage , 2006, IUI '06.

[3]  Filip Radlinski,et al.  Evaluating the accuracy of implicit feedback from clicks and query reformulations in Web search , 2007, TOIS.

[4]  Nils J. Nilsson,et al.  Artificial Intelligence , 1974, IFIP Congress.

[5]  Sung Hoon Jung.  Water Flowing and Shaking Optimization , 2012, Int. J. Fuzzy Log. Intell. Syst..

[6]  Wei-Ying Ma,et al.  Learning block importance models for web pages , 2004, WWW '04.

[7]  Stephen E. Robertson,et al.  Simple BM25 extension to multiple weighted fields , 2004, CIKM '04.

[8]  Rajeev Motwani,et al.  The PageRank Citation Ranking : Bringing Order to the Web , 1999, WWW 1999.

[9]  Weiyi Meng,et al.  Using the Structure of HTML Documents to Improve Retrieval , 1997, USENIX Symposium on Internet Technologies and Systems.

[10]  Ethem Alpaydin,et al.  Introduction to machine learning , 2004, Adaptive computation and machine learning.

[11]  Sanjit Sengupta,et al.  Measuring Users' Web Activity to Evaluate and Enhance Advertising Effectiveness , 2002 .

[12]  Stephen E. Robertson,et al.  Some simple effective approximations to the 2-Poisson model for probabilistic weighted retrieval , 1994, SIGIR '94.

[13]  Sergey Brin,et al.  The Anatomy of a Large-Scale Hypertextual Web Search Engine , 1998, Comput. Networks.

[14]  Albrecht Schmidt,et al.  Knowing the User's Every Move – User Activity Tracking for Website Usability Evaluation and Implicit Interaction , 2006 .

[15]  Mark Claypool,et al.  Implicit interest indicators , 2001, IUI '01.