Developing adaptive systems to fit individual aptitudes
IUI '93 Proceedings of the 1st international conference on Intelligent user interfaces
Fab: content-based, collaborative recommendation
Communications of the ACM
Recommender systems for evaluating computer messages
Communications of the ACM
CSCW '98 Proceedings of the 1998 ACM conference on Computer supported cooperative work
A Framework for Collaborative, Content-Based and Demographic Filtering
Artificial Intelligence Review - Special issue on data mining on the Internet
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Using information scent to model user information needs and actions and the Web
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
From adaptive hypermedia to the adaptive web
Communications of the ACM - The Adaptive Web
E-Commerce Recommendation Applications
Data Mining and Knowledge Discovery
User Modeling and User-Adapted Interaction
Mining customer product ratings for personalized marketing
Decision Support Systems - Special issue: Web data mining
Communications of the ACM - The Blogosphere
Designing for the Social Web (Voices That Matter)
Designing for the Social Web (Voices That Matter)
Adaptive Tourism Modeling and Socialization System
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 04
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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In this paper, we present the tours planning system entitled TOURSPLAN, along with a new lightweight user modelling UM process intended to work as a tourism recommendation system in a commercial environment. The new process tackles issues like cold start, grey sheep and over-specialisation through a rich user model and the application of a gradual forgetting function to the collected user action history. Also, significant performance improvements were achieved regarding the previously proposed UM process.