Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (6): 1898-1913.DOI: 10.11772/j.issn.1001-9081.2021040607
Special Issue: 综述
• Data science and technology • Previous Articles Next Articles
Meng YU, Wentao HE, Xuchuan ZHOU(), Mengtian CUI, Keqi WU, Wenjie ZHOU
Received:
2021-04-19
Revised:
2021-07-14
Accepted:
2021-07-20
Online:
2022-06-22
Published:
2022-06-10
Contact:
Xuchuan ZHOU
About author:
YU Meng, born in 1995, M. S. candidate. Her research interests include recommendation system, information filtering, data mining.Supported by:
通讯作者:
周绪川
作者简介:
于蒙(1995—),女,宁夏固原人,硕士研究生,CCF会员,主要研究方向:推荐系统、信息过滤、数据挖掘基金资助:
CLC Number:
Meng YU, Wentao HE, Xuchuan ZHOU, Mengtian CUI, Keqi WU, Wenjie ZHOU. Review of recommendation system[J]. Journal of Computer Applications, 2022, 42(6): 1898-1913.
于蒙, 何文涛, 周绪川, 崔梦天, 吴克奇, 周文杰. 推荐系统综述[J]. 《计算机应用》唯一官方网站, 2022, 42(6): 1898-1913.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2021040607
推荐技术 | 优点 | 缺点 |
---|---|---|
CB | 1.解决冷启动问题 2.可解释性强 3.易实现 | 1.缺少特征提取的方法 2.易忽略推荐对象的典型性 3.安全性差 |
CF | 1.适合小规模推荐 2.简单易操作 3.易建模 | 1.存在冷启动问题 2.无法处理运算复杂的推荐 3.缺乏可解释性 |
混合 推荐 | 1.克服了数据稀疏 2.弥补不同技术缺点 3.适合用户多的推荐 | 1.缺少高效的混合模式 2.难以建立数学模型 3.推荐过程较复杂 |
Tab. 1 Comparison of advantages and disadvantages of traditional recommendation techniques
推荐技术 | 优点 | 缺点 |
---|---|---|
CB | 1.解决冷启动问题 2.可解释性强 3.易实现 | 1.缺少特征提取的方法 2.易忽略推荐对象的典型性 3.安全性差 |
CF | 1.适合小规模推荐 2.简单易操作 3.易建模 | 1.存在冷启动问题 2.无法处理运算复杂的推荐 3.缺乏可解释性 |
混合 推荐 | 1.克服了数据稀疏 2.弥补不同技术缺点 3.适合用户多的推荐 | 1.缺少高效的混合模式 2.难以建立数学模型 3.推荐过程较复杂 |
模型 | 辅助数据类型 | 主要优点 | 主要难点 | 文献 |
---|---|---|---|---|
DNN | 视频、标签、用户和项目特征 | 1.从多维度学习行为记录特征 2.数据稀疏问题得到了有效解决 3.缓解新用户面临的冷启动问题 | 1.如何使推荐结果更新颖 2.如何建立并且实现非线性特征的推荐模型 | [ |
RNN | 用户和项目特征 | 1.动态地为用户推荐商品 2.时效性强 3.可解释性强 | 1.多源异构数据特征如何有效表达 2.如何为用户和项目的特征动态建模 | [ |
CNN | 图像、视频、 音乐、文本 | 1.有效地利用了辅助信息 2.对用户的隐藏特征进行了挖掘 3.提高了推荐的新颖性 | 1.如何提高推荐模型的训练效率、响应时间以及可扩展性 2.如何建立融入辅助信息的深度学习推荐模型 | [ |
GNN | 会话、文本; 用户-项目特征 | 1.能充分地挖掘节点信息之间的交互信息 2.提高了图节点之间的敏感度 3.可以在图领域对数据特征进行提取。 | 1.如何有效地捕获到图节点的信息传递 2.数据规模较大时,难以进行实时推荐 | [ |
Tab. 2 Literature summary and advantages of different deep learning models
模型 | 辅助数据类型 | 主要优点 | 主要难点 | 文献 |
---|---|---|---|---|
DNN | 视频、标签、用户和项目特征 | 1.从多维度学习行为记录特征 2.数据稀疏问题得到了有效解决 3.缓解新用户面临的冷启动问题 | 1.如何使推荐结果更新颖 2.如何建立并且实现非线性特征的推荐模型 | [ |
RNN | 用户和项目特征 | 1.动态地为用户推荐商品 2.时效性强 3.可解释性强 | 1.多源异构数据特征如何有效表达 2.如何为用户和项目的特征动态建模 | [ |
CNN | 图像、视频、 音乐、文本 | 1.有效地利用了辅助信息 2.对用户的隐藏特征进行了挖掘 3.提高了推荐的新颖性 | 1.如何提高推荐模型的训练效率、响应时间以及可扩展性 2.如何建立融入辅助信息的深度学习推荐模型 | [ |
GNN | 会话、文本; 用户-项目特征 | 1.能充分地挖掘节点信息之间的交互信息 2.提高了图节点之间的敏感度 3.可以在图领域对数据特征进行提取。 | 1.如何有效地捕获到图节点的信息传递 2.数据规模较大时,难以进行实时推荐 | [ |
应用方向 | 深度学习模型 | 数据类型 | 优点 | 未来改进方向 |
---|---|---|---|---|
视频、 图片 推荐 | AM、CNN、RNN、GNN等 | 用户的隐、显式反馈信息,项目内容、用户生成内容、用户-项目的评分矩阵 | 1.对大规模的非线性数据进行处理和计算 2.不存在新项目或者新用户冷启动问题 | 1.建立复杂度较低且高效的模型 2.对异构数据能进行统一的处理(如可以同时输入视频、图片) |
音乐 推荐 | CNN、RNN等 | 用户-项目的评分矩阵、用户画像、社会化标注、项目数据、用户特征 | 1.能动态地为用户进行有效推荐 2.不存在新项目或者新用户冷启动问题 | 1.将用户对音乐的情感表达作为特征属性融合到推荐模型中 2.跨平台获取用户在不同情境下的音乐偏好 |
新闻 推荐 | MLP、RNN、CNN、GCN等 | 目标用户的社会关系图、用户的隐显式反馈信息、知识图谱等 | 1.新闻推荐的时效性高 2.有效地解决数据稀疏的问题 | 1.获取用户短期新闻偏好变化,从而动态地为用户推荐具有时效性的新闻 2.建立机制对虚假、垃圾新闻进行有效屏蔽 |
社交 网络 推荐 | RNN、RNN、CNN、GCN等 | 目标用户的社会关系图、知识图谱、时间数据、位置数据等 | 1.能对社交网络中社交信息的权重进行重新分配 2.能跨平台捕获用户的社交网络 | 1.用户隐私和安全的保护,需推荐系统建立相应的隐私保护机制 2.建立对推荐新颖性、可靠性 、安全性评价指标的评估方法 |
Tab. 3 Comparison of improvement directions of deep learning in different recommendation fields
应用方向 | 深度学习模型 | 数据类型 | 优点 | 未来改进方向 |
---|---|---|---|---|
视频、 图片 推荐 | AM、CNN、RNN、GNN等 | 用户的隐、显式反馈信息,项目内容、用户生成内容、用户-项目的评分矩阵 | 1.对大规模的非线性数据进行处理和计算 2.不存在新项目或者新用户冷启动问题 | 1.建立复杂度较低且高效的模型 2.对异构数据能进行统一的处理(如可以同时输入视频、图片) |
音乐 推荐 | CNN、RNN等 | 用户-项目的评分矩阵、用户画像、社会化标注、项目数据、用户特征 | 1.能动态地为用户进行有效推荐 2.不存在新项目或者新用户冷启动问题 | 1.将用户对音乐的情感表达作为特征属性融合到推荐模型中 2.跨平台获取用户在不同情境下的音乐偏好 |
新闻 推荐 | MLP、RNN、CNN、GCN等 | 目标用户的社会关系图、用户的隐显式反馈信息、知识图谱等 | 1.新闻推荐的时效性高 2.有效地解决数据稀疏的问题 | 1.获取用户短期新闻偏好变化,从而动态地为用户推荐具有时效性的新闻 2.建立机制对虚假、垃圾新闻进行有效屏蔽 |
社交 网络 推荐 | RNN、RNN、CNN、GCN等 | 目标用户的社会关系图、知识图谱、时间数据、位置数据等 | 1.能对社交网络中社交信息的权重进行重新分配 2.能跨平台捕获用户的社交网络 | 1.用户隐私和安全的保护,需推荐系统建立相应的隐私保护机制 2.建立对推荐新颖性、可靠性 、安全性评价指标的评估方法 |
数据集的类型及名称 | 用户数量 | 项目数量 | 评论数量 | 稀疏度/% | 获取链接 | |
---|---|---|---|---|---|---|
电影推荐 | MovieLens 1M | 6 040 | 3 883 | 1 000 209 | 4.26 | https://grouplens.org/datasets/movielens/ |
MovieLens10M | 71 567 | 9 164 | 10 000 054 | 1.3 | https://grouplens.org/datasets/movielens/ | |
MovieLens 20M | 138 493 | 27 278 | 20 000 263 | 0.52 | https://grouplens.org/datasets/movielens/ | |
电子商务 | Epinions | 49 290 | 139 738 | 664 824 | 0.011 | http://www.trustlet.org/wiki/Epinions_datasets |
Amazon | 5 786 | 26 573 | 14 280 000 | 0.002 | http://jmcauley.ucsd.edu/data/amazon/ | |
音乐推荐 | Last.fm | 1 892 | 17 632 | 92 834 | 0.28 | https://grouplens.org/datasets/hetrec-2 011/ |
新闻推荐 | Mind-small | 50 000 | 93 698 | 230 117 | 0.056 | https://msnews.github.io |
Mind | 1 000 000 | 161 013 | 24 155 470 | 0.012 | https://msnews.github.io | |
文本推荐 | Yelp | 2 189 457 | 1 162 119 | 8 635 403 | 0.043 | https://www.yelp.com/dataset |
Goodbooks-10k | 865 456 | 10 000 | 6 000 000 | 0.12 | https://github.com/zygmuntz/goodbooks-10k |
Tab.4 Summarization and statistics of commonly used open datasets
数据集的类型及名称 | 用户数量 | 项目数量 | 评论数量 | 稀疏度/% | 获取链接 | |
---|---|---|---|---|---|---|
电影推荐 | MovieLens 1M | 6 040 | 3 883 | 1 000 209 | 4.26 | https://grouplens.org/datasets/movielens/ |
MovieLens10M | 71 567 | 9 164 | 10 000 054 | 1.3 | https://grouplens.org/datasets/movielens/ | |
MovieLens 20M | 138 493 | 27 278 | 20 000 263 | 0.52 | https://grouplens.org/datasets/movielens/ | |
电子商务 | Epinions | 49 290 | 139 738 | 664 824 | 0.011 | http://www.trustlet.org/wiki/Epinions_datasets |
Amazon | 5 786 | 26 573 | 14 280 000 | 0.002 | http://jmcauley.ucsd.edu/data/amazon/ | |
音乐推荐 | Last.fm | 1 892 | 17 632 | 92 834 | 0.28 | https://grouplens.org/datasets/hetrec-2 011/ |
新闻推荐 | Mind-small | 50 000 | 93 698 | 230 117 | 0.056 | https://msnews.github.io |
Mind | 1 000 000 | 161 013 | 24 155 470 | 0.012 | https://msnews.github.io | |
文本推荐 | Yelp | 2 189 457 | 1 162 119 | 8 635 403 | 0.043 | https://www.yelp.com/dataset |
Goodbooks-10k | 865 456 | 10 000 | 6 000 000 | 0.12 | https://github.com/zygmuntz/goodbooks-10k |
应用 领域 | 推荐 技术 | 代表性模型 | 数据信息类型 | 模型特点 |
---|---|---|---|---|
电影 推荐 | DNN, CF | TransHR[ | 用户信息、电影 信息、观看时间、 评分 | 超关系数据翻译嵌入模型(Translating embedding for Hyper-Relational data, TransHR)更关注电影之间的关系,它将电影之间的关系嵌入到关系空间中,电影之间的多种向量关系能得到保留,但也因此增加了模型的空间复杂度 |
音乐 推荐 | CNN, GRU, CB | PEIA[ 潜在因素模型[ | 音乐信息、歌手 信息、听歌时长、 点击数据 | 整合性格情绪专注的模型(Personality and Emotion Integrated Attentive model, PEIA)是将用户的人格和情感结合的模型,充分利用了用户的兴趣偏好变化和社交数据; 潜在因素模型通过人的听觉效应特征学习用户潜在偏好的音频,处理的音频被输入到CNN模型中,缓解了冷启动问题 |
新闻 推荐 | LSTM, GRU | NPA[ LSTUR[ | 新闻信息、 时间、位置 | 个性化注意力的神经网络新闻推荐(Neural news recommendation with Personalized Attention, NPA)模型主要关注不同用户对同一篇新闻的感兴趣程度,从而利用模型中的注意力机制部分对用户的兴趣建模; 长短期用户表示(Long and Short-Term User Representation, LSTUR)模型融合了用户偏好和时序兴趣,能动态为用户产生新闻推荐 |
社交 网络 | MLP, CF, MF | NSCR[ | 用户信息、时间、位置等环境信息 | 神经社会协作分级(Neural Social Collaborative Ranking, NSCR)模型是将MLP和CF结合的一种深度协同过滤推荐算法,其输入是用户特征信息,经过MLP预测用户潜在偏好 |
视频 推荐 | MLP | YouTube视频 推荐过程[ | 播放量、点击 次数、访问日志 | 文献[ |
广告 推荐 | CF,DL | AdROSA[ FBARS[ | 网页信息、文本 数据、面部信息 | 自适应个性化的网络广告推荐模型(Adaptive personalization of web advertising, AdROSA)通过用户对广告的响应时间和对广告的评论信息捕获用户的潜在偏好; 基于深度学习的人脸广告推荐模型(Face Based Advertisement Recommendation System with deep learning, FBARS)的关键是实现对人脸的三维特征提取,然后将特征提取结果以三维数组的形式传入深度学习推荐模块 |
Tab.5 Applications of recommended techniques in different fields
应用 领域 | 推荐 技术 | 代表性模型 | 数据信息类型 | 模型特点 |
---|---|---|---|---|
电影 推荐 | DNN, CF | TransHR[ | 用户信息、电影 信息、观看时间、 评分 | 超关系数据翻译嵌入模型(Translating embedding for Hyper-Relational data, TransHR)更关注电影之间的关系,它将电影之间的关系嵌入到关系空间中,电影之间的多种向量关系能得到保留,但也因此增加了模型的空间复杂度 |
音乐 推荐 | CNN, GRU, CB | PEIA[ 潜在因素模型[ | 音乐信息、歌手 信息、听歌时长、 点击数据 | 整合性格情绪专注的模型(Personality and Emotion Integrated Attentive model, PEIA)是将用户的人格和情感结合的模型,充分利用了用户的兴趣偏好变化和社交数据; 潜在因素模型通过人的听觉效应特征学习用户潜在偏好的音频,处理的音频被输入到CNN模型中,缓解了冷启动问题 |
新闻 推荐 | LSTM, GRU | NPA[ LSTUR[ | 新闻信息、 时间、位置 | 个性化注意力的神经网络新闻推荐(Neural news recommendation with Personalized Attention, NPA)模型主要关注不同用户对同一篇新闻的感兴趣程度,从而利用模型中的注意力机制部分对用户的兴趣建模; 长短期用户表示(Long and Short-Term User Representation, LSTUR)模型融合了用户偏好和时序兴趣,能动态为用户产生新闻推荐 |
社交 网络 | MLP, CF, MF | NSCR[ | 用户信息、时间、位置等环境信息 | 神经社会协作分级(Neural Social Collaborative Ranking, NSCR)模型是将MLP和CF结合的一种深度协同过滤推荐算法,其输入是用户特征信息,经过MLP预测用户潜在偏好 |
视频 推荐 | MLP | YouTube视频 推荐过程[ | 播放量、点击 次数、访问日志 | 文献[ |
广告 推荐 | CF,DL | AdROSA[ FBARS[ | 网页信息、文本 数据、面部信息 | 自适应个性化的网络广告推荐模型(Adaptive personalization of web advertising, AdROSA)通过用户对广告的响应时间和对广告的评论信息捕获用户的潜在偏好; 基于深度学习的人脸广告推荐模型(Face Based Advertisement Recommendation System with deep learning, FBARS)的关键是实现对人脸的三维特征提取,然后将特征提取结果以三维数组的形式传入深度学习推荐模块 |
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