| 摘 要: 随着互联网上信息量呈指数增长，用户从大量信息中挑选目标信息变成了一种复杂且耗时的作业。为用 户解决因信息量爆炸而不能快速获得目标信息的方法就是构建推荐系统。深度学习作为当前热门的研究话题，在许多领 域都取得了突破性的成就。利用深度学习挖掘用户和物品的隐含属性，构建用户和物品的关系模型，可以提高个性化推 荐的精确度。本文介绍了推荐系统和深度学习，分析了深度学习在推荐领域的应用现状并做出了展望。 |
| 关键词: 推荐系统；深度学习；协同过滤；内容推荐 |
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中图分类号: TP301
文献标识码: A
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| Survey of Deep Learning Applied in Recommendation System |
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LV Gang,ZHANG Wei
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( College of Computer Science and Information Engineering, Harbin Normal University, Harbin 150025, China)
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| Abstract: With the exponential growth of information on the Internet,it becomes a complicated and time-consuming task for users to select target information from a large amount of information.The way for users to solve the problem of not being able to quickly obtain target information due to the explosion of information is to construct Recommended system.As a hot research topic,researches of deep learning have made breakthrough achievements in many fields.Using deep learning to mine the hidden attributes of users and items,and building a relationship model between users and items can improve the accuracy of personalized recommendations.This paper introduces the recommendation system and deep learning,then analyzes the current status of application of deep learning in the recommendation field, and provides research prospects. |
| Keywords: recommendation system;deep learning;collaborative filtering;content-based recommendation |
