| 融合时间偏差信息的邻域型因子分解推荐算法 |
| |
| 引用本文： | 戴月明,周俊宇,吴定会.融合时间偏差信息的邻域型因子分解推荐算法[J].计算机工程与应用,2016,52(4):129-134. |
| |
| 作者姓名： | 戴月明 周俊宇 吴定会 |
| |
| 作者单位： | 江南大学 物联网工程学院，江苏 无锡 214122 |
| |
| 基金项目： | 国家863高技术研究发展计划(No.2013AA040405) |
| |
| 摘 要： | 针对传统推荐算法在进行评分预测时推荐精度低这一问题，提出了融合时间偏差信息的邻域型因子分解推荐算法（简称NFDRA）。它以因子分解算法为主，随机梯度下降寻优为辅，并融合了用户评分的邻域信息以及三种时间偏差信息。实验表明，融合时间偏差的邻域型因子分解推荐算法，相比传统的因子分解推荐可以产生更高精度的推荐结果并具有显著性差异。
|
| 关 键 词： | 时间偏差 因子分解 推荐精度 梯度下降 |
Neighborhood factor decomposition recommendation algorithm based on time deviation information |
| |
| DAI Yueming;ZHOU Junyu;WU Dinghui.Neighborhood factor decomposition recommendation algorithm based on time deviation information[J].Computer Engineering and Applications,2016,52(4):129-134. |
| |
| Authors: | DAI Yueming;ZHOU Junyu;WU Dinghui |
| |
| Affiliation: | School of the Internet of Things, Jiangnan University, Wuxi, Jiangsu 214122, China |
| |
| Abstract: | In view of the problem of low accuracy in the traditional recommendation algorithm. The neighborhood factor decomposition recommendation algorithm based on time deviation information（NFDRA） is presented. The main part of it is factorization algorithm, stochastic gradient descent optimization is complementary, and the integration of user ratings neighborhood information and time deviation. The experiments show, the neighborhood factor decomposition recommendation algorithm based on time deviation information, can get more accurate results and have significant difference. |
| |
| Keywords: | time deviation factorization accuracy of recommendation gradient descent |
| 本文献已被 CNKI 等数据库收录！ |
| 点击此处可从《计算机工程与应用》浏览原始摘要信息 |
|
点击此处可从《计算机工程与应用》下载全文 |
