KMS Chongqing Institute of Green and Intelligent Technology, CAS
| Performance of nonnegative latent factor models with β-distance functions in recommender systems | |
Yuan, Ye ; Luo, Xin
| |
| 2018 | |
| 摘要 | Nonnegative latent factor (NLF) models are able to well represent high-dimensional and sparse (HiDS) matrices filled with nonnegative data, which are frequently encountered in industrial applications like recommender systems. Current NLF models mostly adopt the Euclidean distance or Kullback-Leibler divergence as the objective function, which actually correspond to the special case of β=2 or 1 in β-distance functions. With β not limited in such special cases, an NLF model's performance varies, making it highly attractive to investigate the resultant performance variations. We first divide the ß-distance-based function into three categories, i.e., β=0, β=1, and β≠0 or 1, respectively. Subsequently, we deduce the nonnegative training rules corresponding for different kinds of objectives to achieve different NLF models. Experimental results on industrial matrices indicate that the frequently adopted cases of β=2 or 1 are probably not able to achieve the most accurate or efficient models. It is promising to further improve the performance of NLF models with carefully-tuned β-distance functions as the training objective. © 2018 IEEE. |
| 语种 | 英语 |
| DOI | 10.1109/ICNSC.2018.8361358 |
| 会议(录)名称 | 15th IEEE International Conference on Networking, Sensing and Control, ICNSC 2018
 |
| 页码 | 1-7 |
| 收录类别 | EI |
| 会议地点 | Zhuhai, China |
| 会议日期 | March 27, 2018 - March 29, 2018 |
