Name
Abstract | ||
|---|---|---|
Online retail stores face great challenges to recommend products due to the size and sparsity of the databases, as well as the variety of new users and items. As current techniques, based on collaborative filtering, address those issues with only partial success, the present paper proposes the use of a hybrid system of recommendation in online stores. This system makes use of collaborative filtering and of a fuzzy number model based on marketing concepts. Experimental results show that the proposed system presents great invariance to sparse databases, which is of great value for retail companies. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/FUZZ-IEEE.2012.6251308 | Fuzzy Systems |
Keywords | Field | DocType |
collaborative filtering,fuzzy set theory,recommender systems,retail data processing,collaborative filtering,fuzzy number,hybrid recommendation system,marketing concept,online retail store,sparse database,collaborative filtering,fuzzy numbers,marketing,recommendation | Recommender system,Collaborative filtering,Information retrieval,Invariant (physics),Computer science,Fuzzy set,Artificial intelligence,Fuzzy number,Hybrid system,Machine learning | Conference |
ISSN | ISBN | Citations |
1098-7584 E-ISBN : 978-1-4673-1505-0 | 978-1-4673-1505-0 | 4 |
PageRank | References | Authors |
0.37 | 11 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Miguel A. G. Pinto | 1 | 4 | 0.37 |
| Ricardo Tanscheit | 2 | 118 | 21.53 |
| Marley B. R. Vellasco | 3 | 280 | 47.47 |