[1]程 涛,崔宗敏,喻 静.一种用于视频推荐的基于 LDA 的深度学习模型[J].计算机技术与发展,2020,30(08):86-90.[doi:10. 3969 / j. issn. 1673-629X. 2020. 08. 014]
　CHENG Tao,CUI Zong-min,YU Jing.A LDA-based Topic Attribute-aware in-Depth Learning Model for Video Recommendation[J].,2020,30(08):86-90.[doi:10. 3969 / j. issn. 1673-629X. 2020. 08. 014]
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一种用于视频推荐的基于 LDA 的深度学习模型()
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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08期
页码:
86-90
栏目:
智能、算法、系统工程
出版日期:
2020-08-10

文章信息/Info

Title:
A LDA-based Topic Attribute-aware in-Depth Learning Model for Video Recommendation
文章编号:
1673-629X(2020)08-0086-05
作者:
程 涛; 崔宗敏; 喻 静
九江学院 信息科学与技术学院,江西 九江 332005
Author(s):
CHENG Tao; CUI Zong-min; YU Jing
School of Information Science and Technology,Jiujiang University,Jiujiang 332005,China
关键词:
LDA 主题模型; 深度学习; 隐式反馈; 视频推荐; 个性化推荐
Keywords:
LDA theme model; deep learning; implicit feedback; video recommendation; personalized recommendation
分类号:
TP391.5
DOI:
10. 3969 / j. issn. 1673-629X. 2020. 08. 014
摘要:
随着视频产业的发展,大量的视频已沉积在信息海洋中,为了缓解这种现象,越来越多的推荐算法开始应用于个性化视频推荐。 然而目前的推荐算法都是以协同过滤为自主, 都只注重通过评分矩阵提高捕捉用户与视频的低阶的交互, 忽略了用户兴趣与视频属性的高阶关联。 在这种背景下, 文中通过 LDA 主题模型预测用户兴趣主题,引用干扰词典和关键词典来提高 LDA 模型对视频文本聚类的准确率,然后利用最近提出的神经协同框架建模用户兴趣和视频属性的高阶关联,把 LDA 模型与深度学习相结合提出了一种新的模型 LIVR 用于视频推荐,最后在通过网络爬虫爬取的数据集上验证并对实验结果进行分析。 结果表明,该模型的 Top-N 推荐准确率较常见的几个深度学习模型提高了约 1.9 个百分点。
Abstract:
With the development of video industry,a large amount of videos have been deposited in the information ocean. In order to alleviate this phenome-non, more and more recommendation algorithms have begun to be applied to personalized video recommendation. However,the current recomme-ndation algorithms are all based on collaborative filtering. They only focus on improving the low-level interaction between users and videos through the scoring matrix, ignoring the high - order association between user interests and video attributes. In this context, we use the LDA topic model to predict user interest topics, cite interference dictionaries and key dictionaries to improve the accuracy of LDA model clustering of video texts, and then use the recently proposed neural collaboration framework to model high-order associations of user interests and video attributes. Combined with LDA model and deep learning, a new model LIVR is proposed for video recommendation. Finally, it is verified on the dataset crawled by the web crawler and the experimental results are analyzed. The results show that the Top-N recommendation accuracy of the model is about 1. 9 per-centage points higher than the common deep learning models.

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更新日期/Last Update: 2020-08-10