Name
Abstract | ||
|---|---|---|
Over the past few years, the recommender system has been proposed as a critical role to help users choose the preferred product from a massive amount of data. For music recommendation, most recent recommender systems made attempts to associate music with the user's preferences primarily based on user ratings. However, this kind of recommendation mechanism encounters the problem called rating diversity that makes the prediction results unreliable. To cope with this problem, in this paper, we propose a novel music recommendation approach that utilizes social media tags instead of ratings to calculate the similarity between music pieces. Through the proposed tag-based similarity, the user preferences hidden in tags can be inferred effectively. The empirical evaluations on real social media datasets reveal that our proposed approach using social tags outperforms the existing ones using only ratings in terms of predicting the user's preferences to music. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.procs.2013.09.107 | Procedia Computer Science |
Keywords | Field | DocType |
Social tags mining,music recommendation,collaborative filtering,multimedia data mining | Recommender system,Data mining,World Wide Web,Collaborative filtering,Social media,Computer science,Social tags,Multimedia data mining | Conference |
Volume | ISSN | Citations |
22 | 1877-0509 | 9 |
PageRank | References | Authors |
0.52 | 12 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ja-Hwung Su | 1 | 329 | 24.53 |
| Wei-Yi Chang | 2 | 13 | 0.97 |
| Vincent S. Tseng | 3 | 2923 | 161.33 |