Name
Abstract | ||
|---|---|---|
Recommender systems are popular social web tools, as they address the information overload problem and provide personalization of results [1]. This paper presents a large-scale collaborative approach to the crucial part in the playlist recommendation process: next song recommendation. We show that a simple markov-chain based algorithm improves performance compared to baseline models when neither content, nor user metadata is available. The lack of content-based features makes this task particularly hard. |
Year | Venue | Field |
|---|---|---|
2010 | LWA | Recommender system,Metadata,Information overload,World Wide Web,Social web,Computer science,Personalization |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
8 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andre Busche | 1 | 32 | 4.31 |
| Artus Krohn-Grimberghe | 2 | 76 | 9.97 |
| Lars Schmidt-Thieme | 3 | 3802 | 216.58 |