Name
Abstract | ||
|---|---|---|
Anonymous recommender systems are the electronic pendant to vendors, who ask the customers a few questions and subsequently recommend products based on the answers. In this article we will propose attribute aware classifier-based approaches for such a system and compare it to classifier-based approaches that only make use of the product IDs and to an existing real-life knowledge-based system. We will show that the attribute-based model is very robust against noise and provides good results in a learning over time experiment. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/978-3-540-70981-7_57 | ADVANCES IN DATA ANALYSIS |
Keywords | Field | DocType |
recommender system,knowledge based system | Recommender system,World Wide Web,Ask price,Collaborative filtering,Cold start,Computer science,Association rule learning,Classifier (linguistics) | Conference |
ISSN | Citations | PageRank |
1431-8814 | 0 | 0.34 |
References | Authors | |
9 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Manuel Stritt | 1 | 13 | 1.75 |
| Karen H. L. Tso | 2 | 32 | 3.81 |
| Lars Schmidt-Thieme | 3 | 3802 | 216.58 |