Name
Abstract | ||
|---|---|---|
Recently deep learning based recommendation systems have been actively explored to solve the cold-start problem using a hybrid approach. However, the majority of previous studies proposed a hybrid model where collaborative filtering and content-based filtering modules are independently trained. The end-to-end approach that takes different modality data as input and jointly trains the model can provide better optimization but it has not been fully explored yet. In this work, we propose deep content-user embedding model, a simple and intuitive architecture that combines the user-item interaction and music audio content. We evaluate the model on music recommendation and music auto-tagging tasks. The results show that the proposed model significantly outperforms the previous work. We also discuss various directions to improve the proposed model further. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Information Retrieval | Recommender system,Architecture,Embedding,Collaborative filtering,Information retrieval,Computer science,Filter (signal processing),Artificial intelligence,Deep learning,Train |
DocType | Volume | Citations |
Journal | abs/1807.06786 | 1 |
PageRank | References | Authors |
0.35 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jongpil Lee | 1 | 111 | 15.79 |
| Kyungyun Lee | 2 | 2 | 1.06 |
| Jiyoung Park | 3 | 19 | 3.51 |
| Jangyeon Park | 4 | 6 | 1.47 |
| Juhan Nam | 5 | 261 | 25.12 |