GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Predictive Statistical Models for User Modeling
User Modeling and User-Adapted Interaction
User Modeling for Personalized City Tours
Artificial Intelligence Review
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Ontology-Based User Modeling in an Augmented Audio Reality System for Museums
User Modeling and User-Adapted Interaction
User Modeling and User-Adapted Interaction
Introduction to the special issue on statistical and probabilistic methods for user modeling
User Modeling and User-Adapted Interaction
Adaptive User Modelling and Recommendation in Constrained Physical Environments
AH '08 Proceedings of the 5th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Using interest and transition models to predict visitor locations in museums
AI Communications - Recommender Systems
| Hi-index | 0.00 |
Visitors to cultural heritage sites are often overwhelmed by the information available in the space they are exploring. The challenge is to find items of relevance in the limited time available. Mobile computer systems can provide guidance and point to relevant information by identifying and recommending content that matches a user's interests. In this paper we infer implicit ratings from observed viewing times, and outline a collaborative user modelling approach to predict a user's interests and expected viewing times. We make predictions about viewing themes (item sets) taking into account the visitor's time limit. Our model based on relative interests with imputed ratings yielded the best performance.