Ratings Estimation on Group Recommender Systems
Fuente:
Redalyc
Enregistré dans:
| Auteur principal: | |
|---|---|
| Format: | Artículo científico |
| Langue: | en |
| Publié: |
Asociación Española para la Inteligencia Artificial
2012
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1876479913133867008 |
|---|---|
| author | Ingrid A. Christensen |
| author_facet | Ingrid A. Christensen |
| contents | Ratings Estimation on Group Recommender Systems Ingrid A. Christensen Silvia Schiano Ingeniería Group Proling Aggregate Ratings Ratings Estimation Group Recommendation Recommender systems have been developed to nd personalized content for users. The personalization techniques used in these systems, usually focus on satisfying the needs of individual users. Nevertheless, within some domains there is a need to generate recommendations to groups of users instead of individuals. In order to detect the interest of a group of users, several aggregation techniques have been developed. A disadvantage of these techniques is that they require a large amount of computations to estimate unknown ratings. In this article, we present an analysis of the impact of estimating ratings when an aggregation technique is used. For that purpose, we describe a hybrid approach to generate group recommendations based on group modeling. We also present the results obtained when evaluating the approach and two well-known aggregation techniques in the movie domain, and the variations of those results when the estimation process is not included. 2012 artículo científico 1137-3601 https://www.redalyc.org/articulo.oa?id=92524944003 en http://www.redalyc.org/revista.oa?id=925 Inteligencia Artificial. Revista Iberoamericana de Inteligencia Artificial application/pdf Asociación Española para la Inteligencia Artificial Inteligencia Artificial. Revista Iberoamericana de Inteligencia Artificial (España) Num.50 Vol.15 |
| format | Artículo científico |
| id | redalyc_92524944003 |
| institution | Redalyc |
| language | en |
| publishDate | 2012 |
| publisher | Asociación Española para la Inteligencia Artificial |
| spellingShingle | Ratings Estimation on Group Recommender Systems Ingrid A. Christensen Ingeniería Group Proling Aggregate Ratings Ratings Estimation Group Recommendation Ratings Estimation on Group Recommender Systems Ingrid A. Christensen Silvia Schiano Ingeniería Group Proling Aggregate Ratings Ratings Estimation Group Recommendation Recommender systems have been developed to nd personalized content for users. The personalization techniques used in these systems, usually focus on satisfying the needs of individual users. Nevertheless, within some domains there is a need to generate recommendations to groups of users instead of individuals. In order to detect the interest of a group of users, several aggregation techniques have been developed. A disadvantage of these techniques is that they require a large amount of computations to estimate unknown ratings. In this article, we present an analysis of the impact of estimating ratings when an aggregation technique is used. For that purpose, we describe a hybrid approach to generate group recommendations based on group modeling. We also present the results obtained when evaluating the approach and two well-known aggregation techniques in the movie domain, and the variations of those results when the estimation process is not included. 2012 artículo científico 1137-3601 https://www.redalyc.org/articulo.oa?id=92524944003 en http://www.redalyc.org/revista.oa?id=925 Inteligencia Artificial. Revista Iberoamericana de Inteligencia Artificial application/pdf Asociación Española para la Inteligencia Artificial Inteligencia Artificial. Revista Iberoamericana de Inteligencia Artificial (España) Num.50 Vol.15 |
| title | Ratings Estimation on Group Recommender Systems |
| topic | Ingeniería Group Proling Aggregate Ratings Ratings Estimation Group Recommendation |
| url | https://www.redalyc.org/articulo.oa?id=92524944003 |