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摘要: 群组推荐在信息检索与数据挖掘领域近年来备受关注, 其旨在从海量候选集中挑选出一组用户可能感兴趣的项目. 随着深度学习技术的不断发展, 基于深度学习的群组推荐方法大量涌现. 鉴于此, 首先介绍群组推荐问题的背景知识, 然后系统综述数据获取方法, 全面评述近年来基于深度学习的群组推荐算法, 并进行系统分类与深入分析. 此外, 还归纳了适用于深度学习方法的群组推荐数据集和评价方法, 对各类推荐算法进行对比实验分析与讨论. 最后, 针对本领域的研究难点进行深入探讨, 并提出未来有待深入研究的方向.Abstract: Group recommendation has emerged as a highly active research topic in the fields of information retrieval and data mining in recent years. Its objective is to select a group of items from a large candidate set that is likely to be of interest to a set of users. With the advancement of deep learning, numerous group recommendation methods based on deep learning have been proposed. This paper provides a brief introduction to the background knowledge of this problem. It reviews the methods of data acquisition and conducts a comprehensive review, systematic classification, and in-depth analysis of group recommendation algorithms based on deep learning. In addition, this paper outlines some group recommendation datasets and evaluation methods suitable for deep methods, and conducts comparative experimental analysis and discussion on various recommendation algorithms. Finally, the research challenges in this field were analyzed, and valuable future research directions were discussed.1)
1 1 豆瓣小组 (https://www.douban.com/group/explore ) 是信息获取和分享平台豆瓣网的重要组成部分, 用户可以根据个人兴趣、剧名、电影、宠物、化妆品、摄影、旅行等主题创建或加入各种小组, 与其他成员讨论和分享相关话题. 每个小组都有自己的规则和特色, 成员可以发布帖子、参与讨论、分享观点和经验等.2 Meetup是美国在线社交平台, 通过收集并分析人们的兴趣爱好和住址等信息, 帮助具有共同兴趣的人组成群组并安排线下聚会.3 Weeplaces是一种基于位置的可视化地图服务, 记录了用户在基于地理位置的社交网络中的签到历史. Yelp是美国最大的点评网站, 用户可以在网站中对商户进行评分、提交评论, 以及分享购物体验等.2)2 4 Flickr是一个提供图片存储和分享服务的网站, 用户可以上传、存储和分享自己的照片和视频, 用户还可以在这里浏览他人的作品、参与讨论和互相交流.3)3 5 元路径是一种定义在异构图上的路径模式, 形式化表示为$ {\mathrm{A}}_{1}\stackrel{{\mathrm{R}}_{1}}{\to }{\mathrm{A}}_{2}\stackrel{{\mathrm{R}}_{2}}{\to }\cdots \stackrel{{\mathrm{R}}_{\mathrm{l}}}{\to }{\mathrm{A}}_{l+1} $ (简写为$ {\mathrm{A}}_{1}{\mathrm{A}}_{2}\cdots {\mathrm{A}}_{l+1} $), 可以理解为在$ {\mathrm{A}}_{1} $和$ {\mathrm{A}}_{l+1} $之间是一种混合的链接关系, 该关系可以形式化地表示为$ \mathrm{R}={\mathrm{R}}_{1}\diamond {\mathrm{R}}_{2}\cdots \diamond {\mathrm{R}}_{\mathrm{l}} $. -
图 3 基于深度学习的群组推荐系统框架
Fig. 3 Framework of a deep learning based group recommendation system
图 4 偏好聚合策略与评分聚合策略的过程对比
Fig. 4 Comparison of the process of preference aggregation strategy and rating aggregation strategy
图 12 群组偏好表示方法分类总结
Fig. 12 Classification summary of group preference representation methods
表 1 数学符号说明
Table 1 Explanation of mathematical symbols
符号 说明 $ U=\left\{{u}_{1},\;{u}_{2},\;{\cdots ,\;u}_{n}\right\} $ 用户集合 $ V=\left\{{v}_{1},\;{v}_{2},\;{\cdots ,\;v}_{m}\right\} $ 项目集合 $ G=\left\{{g}_{1},\;{g}_{2},\;{\cdots ,\;g}_{s}\right\} $ 群组集合 $ \mathit{A}={\left[{a}_{li}\right]}_{s\times m} $ $ \langle $群组−项目$\rangle $交互矩阵 $ \mathit{B}={\left[{b}_{ij}\right]}_{n\times m} $ $ \langle $用户−项目$\rangle $交互矩阵 $ \mathit{C}={\left[{c}_{lj}\right]}_{s\times n} $ $ \langle $群组−用户$\rangle $交互矩阵 $ {\mathcal{G}}_{UV} $ $\langle $用户−项目$\rangle $交互图 $ {R}_{V}\left(u\right) $ 与用户$ u $有过交互的所有项目集合 $ \mathscr{p} $ 元路径 $ {\mathcal{N}}^{\mathscr{p}}\left(u\right) $ 节点$ u $基于元路径$ \mathscr{p} $找到的近邻集合 $ {u}_{t}^{g} $ 群组$ g $中的第$ t $个成员, $ {u}_{t}^{g}\in U $ $ f:V\to \mathbf{R} $ 由项目集到实数域的函数 (映射) $ f $ $ {\boldsymbol{e}}_{u},\;{\boldsymbol{e}}_{v},\;{\boldsymbol{e}}_{g} $ 用户$ u $, 项目$ v $和群组$ g $的ID嵌入向量 $ {\boldsymbol{h}}_{u},\;{\boldsymbol{h}}_{v},\;{\boldsymbol{h}}_{g} $ $ {\boldsymbol{e}}_{u},\;{\boldsymbol{e}}_{v},\;{\boldsymbol{e}}_{g} $ 经过编码后的向量表示 
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表 2 群组偏好表示学习方法对比
Table 2 Comparison of learning methods for group preference representation
表示学习层技术 特点 不足 基于启发式聚合策略的群组偏好表示方法 结合个性化推荐方法和预定义的聚合策略完成群组推荐任务, 方法简单高效 无法根据交互数据自身的模式来学习成员之间、成员与群组之间的影响力 基于概率模型的群组偏好表示方法 建模群组的生成过程, 采用潜变量表示用户对群组或其他成员的影响力 较依赖于先验分布的假设, 无法动态地建模成员用户的影响力 基于注意力机制的群组偏好表示方法 采用注意力机制主动从用户交互记录等信息中学习成员的影响力 数据稀疏性可能导致模型训练低效, 使得学习到的影响力不准确 基于图神经网络的群组偏好表示方法 采用图神经网络建模用户、群组和项目之间的高阶交互关系, 并结合注意力算子计算信息沿着关系传播的权重, 有效缓解因数据稀疏导致推荐效果不佳的问题 可能需要用户的社交信息来构建网络, 较难实现; 针对冷启动群组, 需要重新训练网络 增加约束类的群组偏好表示方法 采用增加约束的方式降低解空间的规模, 基于多任务之间的共性特征, 提升模型优化的效果 较依赖于预训练数据集的质量; 较依赖于任务之间的关联强度 引入外部信息的群组偏好表示方法 通过引入外部信息的方式, 增强群组偏好的表示学习, 如社交网络信息、项目描述信息和用户评论信息等 外部信息较难获取等问题
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表 3 群组推荐数据集信息
Table 3 Information of group recommendation dataset
数据集 类别 内容 CAMRa2011[46, 48−49, 54, 62, 66] 电影 包含602个用户组成的290个群组对 7 710 部电影的评分.MovieLens 1M[31, 47, 49, 53−54, 67] 电影 包含上百万评分记录的电影数据集, 由于该数据集不存在显式的群组, 通常根据用户相似度构建群组. Weeplaces[55, 60] 签到 由于该数据集中不存在显式的群组, 通常将15 min以内在同一地点签到且存在朋友关系的用户视为一个群组, 形成包含8 643个用户打卡25 081个商户的22 733个群组. Yelp[32, 53, 55, 60, 68−69] 点评 包含34 504个用户对22 611个餐厅的点评. 由于该数据集不存在显式的群组, 将在同一时间段内打卡同一个餐厅且存在社交关系的用户视为一个群组, 形成24 103个群组. Douban[32, 55, 60, 63, 68−69] 活动 包含70 743个用户对60 028个活动的评分. 由于该数据集不存在显式的群组, 将参加同一活动的用户视为一个群组, 形成109 538个群组. Meetup[31, 47−48, 52, 63, 70] 活动 按照事件的地点, 该数据集分为Meetup-NYC (纽约市) 和Meetup-Cal (加利福尼亚). 这两个数据集均没有显式的群组, 通常将参加同一个事件的人视为一个群组. 其中, Meetup-NYC包含46 619个用户、 9428 个群组、2 326个项目. Meetup-Cal包含59 486个用户、15 207个群组、4 472个项目.BookCrossing[67] 书籍 包含 278 858个用户, 提供271 379本书的1 149 780个评分. 该数据集不包含显式的群组, 通常通过寻找相似用户构建群组. Jester Joke[71] 笑话 包含73 421个用户对 100 个笑话进行的 410 万次评分, 评分范围是 −10 ~ 10 的连续实数. 不包含分组信息, 通过计算用户相似度来进行分组. Netflix[72] 电影 包含480 507个用户对 17 770 部电影的100 480 507条评价数据, 其中评分以5分制为基准. 不包含分组信息, 利用用户的偏好相似信息构造群组.
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表 4 不同表示层算法在三个常见的持续性群组数据集上的推荐效果 (%)
Table 4 The recommendation performance of different presentation layer algorithms on three common persistent group datasets (%)
方法 数据集 CAMRa2011 MS MR H@5 H@10 N@5 N@10 H@5 H@10 N@5 N@10 H@5 H@10 N@5 N@10 NCF-AVG 58.33 77.65 39.69 46.25 59.19 83.15 47.35 52.21 63.52 78.42 45.32 50.29 NCF-LM 57.14 77.13 39.63 45.81 63.31 81.07 45.92 51.19 63.32 78.46 45.18 50.03 NCF-MS 57.19 75.12 38.50 44.41 64.43 82.25 46.62 51.98 62.35 77.85 44.43 49.02 AGREE 58.50 77.93 40.25 46.62 65.96 83.23 47.33 52.94 64.10 79.01 45.76 50.69 MoSAN 58.73 77.51 40.24 46.31 66.41 81.77 47.02 51.63 65.21 79.75 45.23 50.54 GAME 59.09 78.64 40.23 46.70 65.97 83.22 48.38 53.25 65.55 79.32 46.41 50.10 GLIF 59.18 78.93 40.30 46.73 66.43 83.55 48.20 53.44 65.61 79.93 46.43 51.07 KGAG 59.83 79.83 40.35 47.01 66.41 83.55 49.03 54.01 65.80 79.99 46.63 51.39 HetGRec 62.31 81.95 42.33 48.90 68.32 86.15 50.24 55.39 68.01 82.20 48.32 53.39
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表 5 不同算法在三个常见的临时性群组数据集上的推荐效果 (%)
Table 5 The recommendation performance of different algorithms on three common temporary group datasets (%)
方法 数据集 Weeplaces Yelp Douban R@5 R@10 N@5 N@10 R@5 R@10 N@5 N@10 R@5 R@10 N@5 N@10 NCF-AVG 20.91 29.56 11.06 12.90 21.84 29.14 15.08 16.43 35.33 43.23 22.98 24.70 NCF-LM 20.32 28.33 10.49 12.19 23.22 31.44 16.04 17.20 44.29 49.56 31.91 33.10 NCF-MS 19.75 28.72 10.74 12.65 21.38 28.22 14.50 15.08 35.36 42.10 23.04 24.51 AGREE 20.53 29.09 11.40 13.22 24.16 30.98 16.80 17.63 45.95 51.22 33.39 34.57 MoSAN 31.81 37.71 26.25 28.15 46.57 50.61 34.66 36.18 47.10 52.22 36.12 37.24 GAME 41.97 48.53 28.90 30.35 46.44 51.94 35.32 36.52 58.76 77.52 40.29 46.33 KGAG 41.50 48.42 28.96 30.54 46.35 51.87 35.23 36.47 58.64 77.49 40.25 46.29 GroupIM 41.98 48.53 30.35 31.31 48.40 52.39 35.78 36.39 63.54 78.44 45.93 52.19 GBERT 49.43 52.82 35.31 36.43 48.67 53.14 37.46 38.11 65.20 79.90 47.22 54.58
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