Name
Abstract | ||
|---|---|---|
Most of the well-known recommender systems are based on a proprietary database and a finite and well-known set of elements to recommend. This article presents a methodology that allows us to make recommendations on broad domains where we do not have our own database of recommendable items. This methodology proposes the definition of the characteristic aspects of the domain treated and the extraction of information based on online content tools. These tools will provide the system with subjective and objective information that will make it possible to characterize the recommended elements and thus, adapt the recommendations to the user's preferences. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.3233/978-1-61499-900-3-285 | Frontiers in Artificial Intelligence and Applications |
Keywords | Field | DocType |
Recommender system,sentiment analysis,fuzzy aggregation | Information retrieval,Sentiment analysis,Computer science,Fuzzy logic,Theoretical computer science | Conference |
Volume | ISSN | Citations |
303 | 0922-6389 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jesús Serrano-Guerrero | 1 | 59 | 9.31 |
| Antonio Gabriel López-herrera | 2 | 423 | 18.65 |
| Pablo Jimenez | 3 | 0 | 0.34 |
| José Angel Olivas | 4 | 65 | 12.87 |
| Enrique Herrera-Viedma | 5 | 13105 | 642.24 |