Automatic personalization based on Web usage mining
Communications of the ACM
Towards adaptive Web sites: conceptual framework and case study
Artificial Intelligence - Special issue on Intelligent internet systems
ACM SIGKDD Explorations Newsletter
Web mining for web personalization
ACM Transactions on Internet Technology (TOIT)
Web usage mining: discovery and applications of usage patterns from Web data
ACM SIGKDD Explorations Newsletter
Agent technology for personalized information filtering: the PIA-system
Proceedings of the 2005 ACM symposium on Applied computing
A domain model of Web recommender systems based on usage mining and collaborative filtering
Requirements Engineering
Dynamic personalization of web sites without user intervention
Communications of the ACM - Spam and the ongoing battle for the inbox
A Web Usage Mining Framework for Mining Evolving User Profiles in Dynamic Web Sites
IEEE Transactions on Knowledge and Data Engineering
Smart Miner: a new framework for mining large scale web usage data
Proceedings of the 18th international conference on World wide web
Understanding user's query intent with wikipedia
Proceedings of the 18th international conference on World wide web
Collaborative filtering recommender systems
The adaptive web
Content-based recommendation systems
The adaptive web
Applying web usage mining for adaptive intranet navigation
IRFC'11 Proceedings of the Second international conference on Multidisciplinary information retrieval facility
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Personalized or recommender systems are a particular type of information filtering applications. User profiles, representing the information needs and preferences of users, can be inferred from log or clickthrough data, or the ratings that users provide on information items, through their interactions with a system. Such user profiles have been used, for example in iGoogle, to provide personalized recommendations to the users. A user model is a representation of this profile, which can be obtained implicitly through the application of web usage mining techniques. Our work aims to develop Web usage mining tasks to model an intranet or local Web site recommender system. We will focus on the users activity on a university Web site, to customize the contents and structure the presentation of a Web site according to the preferences derived from the user's activity. The customization is based on an individual's user profile as well as a profile representing the collective interest of the entire user community, in this case all users accessing the Web site. The outcome will be personalized recommendations and presentation of a Web site with respect to the user's needs.