Name
Abstract | ||
|---|---|---|
We develop a novel framework, named as $l$ -injection, to address the sparsity problem of recommender systems. By carefully injecting low values to a selected set of unrated user-item pairs in a user-item matrix, we demonstrate that top-N recommendation accuracies of various collaborative filtering (CF) techniques can be significantly and consistently improved. We first adopt the notion of pre-use... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TKDE.2017.2698461 | IEEE Transactions on Knowledge and Data Engineering |
Keywords | Field | DocType |
Motion pictures,Collaboration,Recommender systems,Software,Business,Manganese | Recommender system,Data mining,Singular value decomposition,Collaborative filtering,Computer science,MovieLens,Software,Artificial intelligence,Machine learning | Journal |
Volume | Issue | ISSN |
31 | 1 | 1041-4347 |
Citations | PageRank | References |
3 | 0.45 | 11 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jongwuk Lee | 1 | 7 | 5.61 |
| Won-Seok Hwang | 2 | 91 | 6.87 |
| Juan Parc | 3 | 3 | 0.45 |
| Youngnam Lee | 4 | 4 | 0.81 |
| Sang-Wook Kim | 5 | 792 | 152.77 |
| Dongwon Lee | 6 | 2407 | 190.05 |