Name
Title | ||
|---|---|---|
Using Information Extracted from Microblogs in Order to Palliate the Cold Start Problem in Recommender Systems. |
Abstract | ||
|---|---|---|
Recommender systems predict and recommend items that could be from the user's interest. One of the main problems from recommender systems is the cold start problem, which happens when new users or items are registered into the system. In order to deal with this problem in the literature we can find many proposal. In many cases the solutions require users to provide explicit information which requires effort on their part. For that reason and given the great boom of social networks, we focus on systems that make use of external data sources. Then, in this paper will be presented an approach in which using social media data will classify the users and create some predictions by using classification trees. Therefore the users will not need to explicitly provide any data other than the source of their social media, helping in this way to alleviate the cold start problem, because the system always has information available to establish user profiles. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.3233/978-1-61499-900-3-347 | Frontiers in Artificial Intelligence and Applications |
Keywords | Field | DocType |
Recommender systems,cold-start problem,social media,decision tree classifier | Recommender system,Social media,Information retrieval,Cold start,Computer science,Microblogging,Theoretical computer science | Conference |
Volume | ISSN | Citations |
303 | 0922-6389 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Julio Herce-Zelaya | 1 | 5 | 2.07 |
| Carlos Porcel | 2 | 450 | 24.12 |
| Álvaro Tejeda-Lorente | 3 | 97 | 7.88 |
| Juan Bernabé-Moreno | 4 | 19 | 5.62 |
| Enrique Herrera-Viedma | 5 | 13105 | 642.24 |