Name
Abstract | ||
|---|---|---|
Recommender systems typically require feedback from the user to learn the user's taste This feedback can come in two forms: explicit and implicit Explicit feedback consists of ratings provided by the user for a number of items, while implicit feedback comes from observing user actions on items These actions have to be interpreted by the recommender system and translated into a rating In this paper we propose a method to learn how to translate user actions on items to ratings on these items by correlating user actions with explicit feedback We do this by associating user actions to rated items and subsequently applying naive Bayesian classification to rate new items with which the user has interacted We apply and evaluate our method on data from a web-based music service and we show its potential as an addition to explicit rating. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-13470-8_32 | UMAP |
Keywords | Field | DocType |
implicit explicit feedback,web-based music service,recommender system,user action,explicit rating,explicit feedback,correlating user action,naive bayesian classification,music recommender system,implicit feedback,new item,personalized implicit learning,recommender systems,bayesian classification | Recommender system,World Wide Web,Naive Bayes classifier,Computer science,Implicit learning,Human–computer interaction | Conference |
Volume | ISSN | ISBN |
6075 | 0302-9743 | 3-642-13469-6 |
Citations | PageRank | References |
4 | 0.49 | 5 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Suzana Kordumova | 1 | 4 | 0.49 |
| Ivana Kostadinovska | 2 | 4 | 0.49 |
| Mauro Barbieri | 3 | 147 | 10.74 |
| V. Pronk | 4 | 54 | 8.29 |
| Jan Korst | 5 | 175 | 19.94 |