Personalized Web search results with profile comparisons

The information explosion on the Internet makes it hard for users to obtain required information from the Web searched results in a more personalized way. For the same input word, most search engines return the same result to each user without taking into consideration user preference. For many users, it is no longer sufficient to get non-customized results. It is crucial to analyze users' search and browsing behaviors based on searching keywords entered by users, the clicking rate of each link in the result and the time they spend on each site. To this end, we have proposed a method to derive user searching profiles. We have also proposed a mechanism to derive document profiles, based on similarity score of documents. In this paper, we discuss how to use our model to combine the user searching profiles and the document profile, with a view to presenting customized search results to the users.

[1]  Hector Garcia-Molina,et al.  Efficient Crawling Through URL Ordering , 1998, Comput. Networks.

[2]  Peter B. Danzig,et al.  Scalable Internet resource discovery: research problems and approaches , 1994, CACM.

[3]  Ben Soh,et al.  Using element and document profile for information clustering , 2004, IEEE International Conference on e-Technology, e-Commerce and e-Service, 2004. EEE '04. 2004.

[4]  Marti A. Hearst,et al.  Reexamining the cluster hypothesis: scatter/gather on retrieval results , 1996, SIGIR '96.

[5]  Oren Etzioni,et al.  Grouper: A Dynamic Clustering Interface to Web Search Results , 1999, Comput. Networks.

[6]  Jacek Gwizdka,et al.  Discriminating Meta-Search: A Framework for Evaluation , 1999, Inf. Process. Manag..

[7]  Andreas Stafylopatis,et al.  A Fuzzy Rule-Based Agent for Web Retrieval-Filtering , 2001, Web Intelligence.

[8]  Oliver A. McBryan,et al.  GENVL and WWWW: Tools for taming the Web , 1994, WWW Spring 1994.

[9]  Brian Amento,et al.  A System for Sharing Recommendations , 1997 .

[10]  A. Mishra,et al.  A self-adaptive clustering based algorithm for increased energy-efficiency and scalability in wireless sensor networks , 2003, 2003 IEEE 58th Vehicular Technology Conference. VTC 2003-Fall (IEEE Cat. No.03CH37484).

[11]  Nicholas J. Belkin,et al.  Evaluation of a tool for visualization of information retrieval results , 1996, SIGIR '96.

[12]  Marti A. Hearst.  TileBars: visualization of term distribution information in full text information access , 1995, CHI '95.

[13]  Xiannong Meng,et al.  Personalized Web Search with Clusters , 2003, International Conference on Internet Computing.

[14]  Shivakumar Vaithyanathan,et al.  Exploiting clustering and phrases for context-based information retrieval , 1997, SIGIR '97.

[15]  Loren Terveen,et al.  PHOAKS: a system for sharing recommendations , 1997, CACM.

[16]  Vincent Kanade,et al.  Clustering Algorithms , 2021, Wireless RF Energy Transfer in the Massive IoT Era.

[17]  Ricardo Baeza-Yates,et al.  Information Retrieval: Data Structures and Algorithms , 1992 .