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ACM Transactions on Internet Technology (TOIT), Volume 7 Issue 4, October 2007

Introduction to intelligent techniques for Web personalization
Sarabjot Singh Anand, Bamshad Mobasher
Article No.: 18
DOI: 10.1145/1278366.1278367

Mining User preference using Spy voting for search engine personalization
Wilfred Ng, Lin Deng, Dik Lun Lee
Article No.: 19
DOI: 10.1145/1278366.1278368

This article addresses search engine personalization. We present a new approach to mining a user's preferences on the search results from clickthrough data and using the discovered preferences to adapt the search engine's ranking function for...

Supporting intelligent Web search
Maurice Coyle, Barry Smyth
Article No.: 20
DOI: 10.1145/1278366.1278369

Search engines continue to struggle to provide everyday users with a service capable of delivering focussed results that are relevant to their information needs. Moreover, traditional search engines really only provide users with a starting point...

Web site personalization based on link analysis and navigational patterns
Magdalini Eirinaki, Michalis Vazirgiannis
Article No.: 21
DOI: 10.1145/1278366.1278370

The continuous growth in the size and use of the World Wide Web imposes new methods of design and development of online information services. The need for predicting the users' needs in order to improve the usability and user retention of a Web...

Generating semantically enriched user profiles for Web personalization
Sarabjot Singh Anand, Patricia Kearney, Mary Shapcott
Article No.: 22
DOI: 10.1145/1278366.1278371

Traditional collaborative filtering generates recommendations for the active user based solely on ratings of items by other users. However, most businesses today have item ontologies that provide a useful source of content descriptors that can be...

Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness
Bamshad Mobasher, Robin Burke, Runa Bhaumik, Chad Williams
Article No.: 23
DOI: 10.1145/1278366.1278372

Publicly accessible adaptive systems such as collaborative recommender systems present a security problem. Attackers, who cannot be readily distinguished from ordinary users, may inject biased profiles in an attempt to force a system to...