An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
Being accurate is not enough: how accuracy metrics have hurt recommender systems
CHI '06 Extended Abstracts on Human Factors in Computing Systems
A hybrid approach for improving predictive accuracy of collaborative filtering algorithms
User Modeling and User-Adapted Interaction
Enhancing privacy and preserving accuracy of a distributed collaborative filtering
Proceedings of the 2007 ACM conference on Recommender systems
An Evaluation Methodology for Collaborative Recommender Systems
AXMEDIS '08 Proceedings of the 2008 International Conference on Automated solutions for Cross Media Content and Multi-channel Distribution
Learning preferences of new users in recommender systems: an information theoretic approach
ACM SIGKDD Explorations Newsletter
Interfaces for eliciting new user preferences in recommender systems
UM'03 Proceedings of the 9th international conference on User modeling
Performance of recommender algorithms on top-n recommendation tasks
Proceedings of the fourth ACM conference on Recommender systems
Comparative evaluation of recommender system quality
CHI '11 Extended Abstracts on Human Factors in Computing Systems
Looking for "good" recommendations: a comparative evaluation of recommender systems
INTERACT'11 Proceedings of the 13th IFIP TC 13 international conference on Human-computer interaction - Volume Part III
A user-centric evaluation framework for recommender systems
Proceedings of the fifth ACM conference on Recommender systems
ACM Transactions on Interactive Intelligent Systems (TiiS)
| Hi-index | 0.00 |
A Recommender System (RS) filters a large amount of information to identify the items that are likely to be more interesting and attractive to a user. Recommendations are inferred on the basis of different user profile characteristics, in most cases including explicit ratings on a sample of suggested elements. RS research highlights that profile length, i. e., the number of collected ratings, is positively correlated to the accuracy of recommendations, which is considered an important quality factor for RSs. Still, gathering ratings adds a burden on the user, which may negatively affect the UX. A design tension seems to exist, induced by two conflicting requirements -- to raise accuracy by increasing the profile length, and to make the profiling process smooth for the user by limiting the number of ratings. The paper presents a wide empirical study (1080 users involved) which explores this issue. Our work attempts to identify which of the two contrasting forces influenced by profile length -- recommendations accuracy and burden of the rating process - has stronger effects on the perceived quality of the UX with a RS.