Name
Abstract | ||
|---|---|---|
Top-N recommendation tasks aim to solve the information overload problem for users in the information age. As a user's decision may be affected by correlations among items, we incorporate such correlations with the user and item latent factors to propose a Poisson-regression-based method for top-N recommendation tasks. By placing priori knowledge and using a sparse structure assumption, this method learns the latent factors and the structure of the item-item correlation matrix through the alternating direction method of multipliers (ADMM). The preliminary experimental results on two real-world datasets show the improved performance of our approach. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3077136.3080670 | SIGIR |
Keywords | Field | DocType |
Recommender systems, poisson regression, item-item correlations | Recommender system,Data mining,Information overload,Computer science,Poisson regression,Artificial intelligence,Covariance matrix,Information Age,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4503-5022-8 | 0 | 0.34 |
References | Authors | |
12 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiajin Huang | 1 | 69 | 15.70 |
| Jian Wang | 2 | 179 | 73.08 |
| Ning Zhong | 3 | 2907 | 300.63 |