Name
Abstract | ||
|---|---|---|
A key challenge in recommender systems is how to profile new users. A well-known solution for this problem is to use active learning techniques and ask the new user to rate a few items to reveal her preferences. The sequence of queries should not be static, i.e in each step the best query depends on the responses of the new user to the previous queries. Decision trees have been proposed to capture the dynamic aspect of this process. In this paper we improve decision trees in two ways. First, we propose the Most Popular Sampling (MPS) method to increase the speed of the tree construction. In each node, instead of checking all candidate items, only those which are popular among users associated with the node are examined. Second, we develop a new algorithm to build decision trees. It is called Factorized Decision Trees (FDT) and exploits matrix factorization to predict the ratings at nodes of the tree. The experimental results on the Netflix dataset show that both contributions are successful. The MPS increases the speed of the tree construction without harming the accuracy. And FDT improves the accuracy of rating predictions especially in the last queries. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICTAI.2013.67 | Tools with Artificial Intelligence |
Keywords | Field | DocType |
decision trees,learning (artificial intelligence),matrix decomposition,query processing,recommender systems,sampling methods,FDT,MPS,Netflix dataset,active learning techniques,factorized decision trees,matrix factorization,most popular sampling method,new user profiling,query sequence,rating predictions,recommender systems,tree construction | Recommender system,Data mining,Decision tree,Active learning,Ask price,Computer science,Matrix decomposition,Exploit,Artificial intelligence,Machine learning,Decision tree learning,Incremental decision tree | Conference |
ISSN | ISBN | Citations |
1082-3409 | 978-1-4799-2971-9 | 5 |
PageRank | References | Authors |
0.46 | 14 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rasoul Karimi | 1 | 49 | 4.18 |
| Martin Wistuba | 2 | 154 | 19.66 |
| Alexandros Nanopoulos | 3 | 1856 | 95.35 |
| Lars Schmidt-Thieme | 4 | 3802 | 216.58 |