Name
Abstract | ||
|---|---|---|
Socially-based recommendation systems have recently attracted significant interest, and a number of studies have shown that social information can dramatically improve a system's predictions of user interests. Meanwhile, there are now many potential applications that involve aspects of both recommendation and information retrieval, and the task of collaborative retrieval-a combination of these two traditional problems-has recently been introduced. Successful collaborative retrieval requires overcoming severe data sparsity, making additional sources of information, such as social graphs, particularly valuable. In this paper we propose a new model for collaborative retrieval, and show that our algorithm outperforms current state-of-the-art approaches by incorporating information from social networks. We also provide empirical analyses of the ways in which cultural interests propagate along a social graph using a real-world music dataset. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/JSTSP.2014.2317286 | J. Sel. Topics Signal Processing |
Keywords | DocType | Volume |
current state-of-the-art approach,collaborative filtering,successful collaborative retrieval,history,cultural interest propagation,machine learning algorithms,social network,social networks,socially-based recommendation systems,social graphs,information retrieval,system prediction improvement,recommender systems,data sparsity,graph theory,additional source,social collaborative retrieval,social networking (online),user interests,social graph,socially-based recommendation system,social information,collaborative retrieval,cultural interests propagate,real-world music dataset | Conference | 8 |
Issue | ISSN | Citations |
4 | 1932-4553 | 0 |
PageRank | References | Authors |
0.34 | 21 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ko-Jen Hsiao | 1 | 19 | 2.64 |
| Alex Kulesza | 2 | 1376 | 64.97 |
| Alfred O. Hero III | 3 | 2600 | 301.12 |