[1]康嘉钰,苏凡军.基于生成对抗网络的长短兴趣推荐模型[J].计算机技术与发展,2020,30(06):35-39.[doi:10. 3969 / j. issn. 1673-629X. 2020. 06. 007]
　KANG Jia-yu,SU Fan-jun.A Long-short-term Interests Recommendation Model Based on Generative Adversarial Networks[J].COMPUTER TECHNOLOGY AND DEVELOPMENT,2020,30(06):35-39.[doi:10. 3969 / j. issn. 1673-629X. 2020. 06. 007]
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基于生成对抗网络的长短兴趣推荐模型()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
30
期数:
2020年06期
页码:
35-39
栏目:
智能、算法、系统工程
出版日期:
2020-06-10

文章信息/Info

Title:
A Long-short-term Interests Recommendation Model Based on Generative Adversarial Networks
文章编号:
1673-629X(2020)06-0035-05
作者:
康嘉钰; 苏凡军
上海理工大学 光电信息与计算机工程学院,上海 200093
Author(s):
KANG Jia-yu; SU Fan-jun
School of Optical-electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China
关键词:
推荐算法; 生成对抗网络; 循环神经网络; 孪生网络; 对比损失函数
Keywords:
recommendation algorithm; generative adversarial networks; recurrent neural network; siamese network; contrastive loss function
分类号:
TP391
DOI:
10. 3969 / j. issn. 1673-629X. 2020. 06. 007
摘要:
传统的推荐系统中,用户的兴趣被认为是稳定不变的,而事实上,用户的兴趣会因为各种因素产生变化。为了更加有利地跟踪用户兴趣偏好变化进行内容推荐,提出了一种基于生成对抗网络的推荐算法 L-GAN (long short-term memory via generative adversarial networks),利用长期和短期的兴趣偏好,通过生成对抗的训练策略来训练推荐模型,使推荐模型产生的推荐列表更加准确。在对抗训练过程中,将数据分为多个行为周期,按照时间顺序依次输入每个行为周期内的用户-项目评价矩阵,生成器模型产生推荐列表,而判断器模型则区分输入的推荐列表是否与真实历史记录的特征相似。最终,通过在两个公开的数据集上与多个推荐模型进行对比实验,结果表明在不同稀疏度的数据集上,L-GAN 算法在推荐精度方面有较明显的提高,更善于挖掘数据的隐层特征。
Abstract:
In the traditional recommendation system,users’ interests are considered to be stable and unchanged, but in fact, they will change due to various factors. In order to track the changes of users’ interests and preferences more advantageously for content recommendation,we propose a recomm-endation algorithm L-GAN based on generating antagonistic network,which takes advantage of long-term and short-term interest preference to train the recommendation model by generating antagonistic training strategies,so that the recommendation list generated by the recommendation model can be more accurate. In the course of confrontation training, the data is divided into several action cycles,and the user-project evaluation matrix is input in time sequence in each action cycle. The generator model generates the recommendation list,while the judge model distinguishes whether the input recommendation list is similar to the characteristics of the real history record. Finally,by comparing the two open datasets with several recommendation models,the experimental results show that the L-GAN algorithm improves the recommendation accuracy significantly in different sparse datasets and is better at mining hidden features of data.

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更新日期/Last Update: 2020-06-10