GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Fab: content-based, collaborative recommendation
Communications of the ACM
Learning and Revising User Profiles: The Identification ofInteresting Web Sites
Machine Learning - Special issue on multistrategy learning
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
User modeling in adaptive interfaces
UM '99 Proceedings of the seventh international conference on User modeling
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Machine Learning for User Modeling
User Modeling and User-Adapted Interaction
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
The Case against Accuracy Estimation for Comparing Induction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
Evaluating the intrusion cost of recommending in recommender systems
UM'05 Proceedings of the 10th international conference on User Modeling
Expert Systems with Applications: An International Journal
A literature review and classification of recommender systems research
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
In this paper, we study the recommendation problem as formed by two tasks: (i) to filter useful/interesting items, (ii) to guide the user to good recommendations. The first task has been widely studied in the field of recommender systems. In fact, the most common characterization of these systems is based on the algorithms that select (filter) the items to be recommended (e.g. collaborative filtering, content-based, etc.). Through this paper, we will focus on the second task: the task of guiding the user. We claim that this task needs a closer attention. In this paper, we report an experiment to provide evidence for this fact. Actually, the experiment shows that machine learning algorithms commonly applied to the first task become useless when applied to the task of guiding.