Name
Abstract | ||
|---|---|---|
Recommendation systems have been integrated into the majority of large online systems to filter and rank information according to user profiles. It thus influences the way users interact with the system and, as a consequence, bias the evaluation of the performance of a recommendation algorithm computed using historical data (via offline evaluation). This paper describes this bias and discuss the relevance of a weighted offline evaluation to reduce this bias for different classes of recommendation algorithms. |
Year | Venue | Field |
|---|---|---|
2015 | ICDM (Posters) | Recommender system,Data mining,Information retrieval,Computer science,Algorithm |
DocType | Volume | ISSN |
Journal | abs/1511.01280 | Petra Perner. 11th Industrial Conference on Data Mining, ICDM
2015, Jul 2015, Hamburg, Germany. Ibai Publishing, pp.57-70, 2015, Advances
in Data Mining |
Citations | PageRank | References |
1 | 0.48 | 6 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Arnaud De Myttenaere | 1 | 27 | 2.74 |
| Boris Golden | 2 | 27 | 2.74 |
| Bénédicte Le Grand | 3 | 126 | 18.50 |
| Fabrice Rossi | 4 | 28 | 3.09 |