Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
Social information filtering: algorithms for automating “word of mouth”
CHI '95 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
IEEE Transactions on Knowledge and Data Engineering
Being accurate is not enough: how accuracy metrics have hurt recommender systems
CHI '06 Extended Abstracts on Human Factors in Computing Systems
Branching and bounds tighteningtechniques for non-convex MINLP
Optimization Methods & Software - GLOBAL OPTIMIZATION
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
| Hi-index | 0.00 |
In the Recommender Systems field ensemble techniques gain growing interest. This approach is based on the idea of mixing many recommenders and to get an average prediction from all of them. Even if it is useful this process may be very expensive from a computational point of view. We propose the use of Operations Research techniques in order to optimize the balance of different predictors and to accelerate it. We show that this problem can be generalized, thus we provide a mathematical framework which helps to find further improvements.