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Data Analysis and Knowledge Discovery  2024, Vol. 8 Issue (2): 1-16    DOI: 10.11925/infotech.2096-3467.2022.1282
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A Survey on Session-Based Recommendation Methods with Graph Neural Network
Zhang Xiongtao1,Zhu Na2(),Guo Yuhui3
1School of Economics and Management, University of Science and Technology Beijing, Beijing 100083, China
2School of National Governance, Southwest University, Chongqing 400715, China
3School of Information, Renmin University of China, Beijing 100872, China
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Abstract  

[Objective] This paper focuses on graph neural network technology,reviewing session-based recommendation methods to provide a reference for future research. [Coverage] We took “session-based recommendation” and “graph neural network” as search terms, and 82 domestic and foreign literatures were screened from databases such as “Web of Science” and “China National Knowledge Infrastructure”. [Methods] From the perspective of framework, evaluation and trend, this paper generalises and compares session-based recommendation methods based on graph neural networks, summarises the existing evaluation resources and discusses the future research trend. [Results] Graph Neural Network is the mainstream technology for implementing session-based recommender systems. The studies on session-based recommendation methods with graph neural network mainly focus on three core problems, session graph construction, session graph learning and session interest representation. [Limitations] Session-based recommendation methods with graph neural networks are constantly emerging, and the research reviewed is only the typical research and not all studies are listed. Future research can be deepened in terms of interpretability, robustness, diversity and fairness. [Conclusions] Graph Neural Network is the mainstream technology for session-based recommender systems. Existing research has conducted preliminary exploration from various aspects and provided sufficient evaluation resources. Future research should combine the characteristics of session recommendation scenarios and develop graph neural network technology to further improve the existing research deficiencies.
Key words： Graph Neural Network      Session-Based Recommendation      Sequential Recommendation      Recommender Systems      Deep Learning     
Received: 03 December 2022      Published: 12 September 2023
ZTFLH:  TP393  
  G250  
Fund:National Social Science Youth Foundation(20CTQ029);Key Project of Humanities and Social Science Research in Hebei Province(ZD202102)
Corresponding Authors: Zhu Na，ORCID：0000-0002-8425-1231，E-mail： zhunank@126.com。   
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Xiongtao Zhang
Na Zhu
Yuhui Guo

Cite this article:

Zhang Xiongtao, Zhu Na, Guo Yuhui. A Survey on Session-Based Recommendation Methods with Graph Neural Network. Data Analysis and Knowledge Discovery, 2024, 8(2): 1-16.

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https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2022.1282     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2024/V8/I2/1

Research Statistics of Different Session-Based Recommendation Methods
Research Framework of Session-Based Recommendation Method with GNN
Schematic of Session Graph Construction Methods
图构建方法 研究思路 优点/缺点
基于单会话的
图构建方法		 基于会话内连续关系 将单个会话内的每个项目视作图中的节点，通过在两个连续项目之间添加边构建会话图模型。 ? 优点：操作相对直接，构建逻辑简单；
? 缺点：无法克服会话数据固有的稀疏性问题。
考虑会话内非连续关系 利用非连续项目关系弥补连续项目关系数据的稀疏性，以综合连续和非连续关系构建会话图模型。 ? 优点：利用非连续关系有效稠密化会话图结构；
? 缺点：无法克服会话数据固有的稀疏性问题。
基于多会话的
图构建方法		 基于超图 通过规定超边为项目建立联系，以有效利用项目之间的非成对关系构建会话图模型。 ? 优点：利用项目之间的非成对关系有效丰富会话图结构；
? 缺点：在时序关系利用上存在限制。
基于常规图 利用额外会话中的项目关系来丰富单个会话中的项目转移关系，以有效利用跨会话项目关系。 ? 优点：利用跨会话项目转移关系有效丰富会话图结构；
? 缺点：增加了噪声信息引入的风险。
Research Ideas， Advantages and Disadvantages of Session Graph Construction Methods
Schematic of Session Graph Learning Methods
图学习方法 研究思路 优点/缺点
基于GCN 采用池化操作等同地对所有邻居节点进行信息聚合，然后以聚合信息更新中心节点表示。 ? 优点：具有简单的运算逻辑；
? 缺点：不适用于大规模会话图学习。
基于GraphSAGE 为每个中心节点采样固定数量的邻居，然后采用池化操作对邻居信息进行聚合，进而结合聚合的邻居信息对中心节点表示进行更新。 ? 优点：具有较强的可扩展性和灵活性；
? 缺点：在信息传播过程中无法合理区分不同邻居节点对中心节点的重要性。
基于GAT 利用注意力机制差异化整合节点的邻居节点信息，然后结合邻居节点信息更新中心节点的表示。 ? 优点：在信息传播过程中，结合邻居节点权重更准确地学习会话图；
? 缺点：在顺序感知的会话图学习方面存在限制。
基于GGNN 将中心节点的前项邻居和后项邻居信息进行聚合，然后将聚合信息输入GRU实现节点表示更新。 ? 优点：在会话图学习的过程中有效地考虑项目之间的顺序关联；
? 缺点：需要搭配合理的情景建模方法才可实现有效的会话推荐。
Research Ideas， Advantages and Disadvantages of Session Graph Learning Methods
Schematic of Session Interest Representation Methods
会话兴趣表示方法 研究思路 优点/缺点
基于注意力 将会话中最后一次交互视作局部会话兴趣；将局部会话兴趣视作查询项，采用注意力机制对会话中所有项目进行加权聚合，获得全局会话兴趣；融合局部和全局会话兴趣，得到完整的会话兴趣表示。 ? 优点：通过差异化整合会话中的项目表示更准确地表示会话兴趣；
? 缺点：仅利用注意力机制会忽略项目在会话中的位置信息。
基于RNN 采用RNN获取各个项目的隐藏状态，通过整合各个项目的隐藏状态输出会话兴趣的最终表示。 ? 优点：利用项目之间的顺序依赖建模会话兴趣表示；
? 缺点：无法有效克服噪声项对会话兴趣表示造成的影响。
基于位置 将项目在当前会话中的位置编码为位置向量，然后组合位置向量与项目表示，将位置信息融入项目表示。 ? 优点：将位置信息融入项目表示，实现更准确的会话兴趣建模；
? 缺点：无法综合会话内项目间关系完整表示会话兴趣。
Research Ideas， Advantages and Disadvantages of Session Interest Learning Methods
实现过程 方法 代表性工作
会话图构建 基于单会话的图构建方法 文献[13]， 文献[19]， 文献[20]等
基于多会话的图构建方法 文献[21]， 文献[25]， 文献[27]等
会话图学习 基于GCN的图学习方法 文献[39]， 文献[40]， 文献[41]等
基于GraphSAGE的图学习方法 文献[21]， 文献[43]， 文献[48]等
基于GAT的图学习方法 文献[14]， 文献[30]， 文献[49]等
基于GGNN的图学习方法 文献[13]， 文献[19]， 文献[20]等
会话兴趣学习 基于注意力机制的会话兴趣学习方法 文献[13]， 文献[39]， 文献[40]等
基于循环神经网络的会话兴趣学习方法 文献[44]， 文献[48]， 文献[49]等
基于位置的会话兴趣学习方法 文献[50]， 文献[51]， 文献[52]等
Summary of Representative Works in the Research Framework
应用领域 数据集名称 会话数量 交互数量 项目数量 平均长度 数据集获取网址
电子
商务
推荐		 Yoochoose 1 375 128 5 426 961 28 582 3.95 https://www.kaggle.com/chadgostopp/recsys-challenge-2015
Tmall 1 774 729 13 418 695 425 348 7.56 https://tianchi.aliyun.com/dataset/dataDetail?dataId=42
Diginetica 780 328 982 961 43 097 5.12 https://competitions.codalab.org/competitions/11161
RetailRocket 59 962 212 182 31 968 3.54 https://www.kaggle.com/datasets/retailrocket/ecommerce-dataset
新闻
推荐		 GLEF 2017 16 444 442 5 540 486 742 3.37 https://www.newsreelchallenge.org/dataset
Globo 1 031 167 2 930 849 13 092 2.84 https://www.newsreelchallenge.org/dataset
Adressa 16G 2 215 62 908 6 765 28.4 http://reclab.idi.ntnu.no/dataset
音乐
推荐		 Last.FM 169 576 2 887 349 449 037 17.03 http://millionsongdataset.com/lastfm/
Nowplaying 27 005 271 177 75 169 10.04 https://www.kaggle.com/chelseapower/nowplayingrs
地点
推荐		 Gowalla * 245 157 6 871 * http://snap.stanford.edu/data/loc-gowalla.html
Foursquare * 155 365 2 675 * https://www.kaggle.com/chetanism/foursquare-nyc-and-tokyo-checkin-dataset
Yelp * 6 990 280 150 346 * https://www.kaggle.com/datasets/yelp-dataset/yelp-dataset
影视
推荐		 Movielens-1M * 1 000 209 3 706 * http://files.grouplens.org/datasets/movielens/
Kwai * 775 834 643 310 681 * https://cloud.tsinghua.edu.cn/d/eb0fc2ebab8d42189f3b/
Tiktok * 1 253 112 7 085 * https://www.kaggle.com/datasets/erikvdven/tiktoktrendingdecember-2020
Statistics for Typical Datasets
类别 模型 主要技术 年份 来源简称 源码获取网址
传统会话推荐模型 POP[59] 统计推断 — — https://github.com/rn5l/session-rec
Item-KNN[5] K最邻近法 2001 WWW* https://github.com/rn5l/session-rec
FPMC[6] 个性化分解的MC 2010 WWW* https://github.com/khesui/FPMC
基于RNN的
会话推荐模型		 NARM[10] 基于注意力机制的RNN 2017 CIKM* https://github.com/rn5l/session-rec
STAMP[60] 基于短期注意和记忆优先的RNN 2018 KDD* https://github.com/rn5l/session-rec
GRU4REC[9] 基于门控循环单元的RNN 2016 ICLR* https://github.com/rn5l/session-re
基于GNN的
会话推荐模型		 SR-GNN[13] 融合软注意力机制的GGNN 2019 AAAI* https://github.com/CRIPAC-DIG/SR-GNN
TAGNN[20] 融合目标注意力机制的GGNN 2020 SIGIR* https://github.com/CRIPAC-DIG/TAGNN
GCE-GNN[14] 融合位置感知注意力机制的GAT 2020 SIGIR* https://github.com/CCIIPLab/GCE-GNN
Illustration of Key Information for Representative Baselines
评价资源 分类 代表性工作
数据集 电子商务领域 Yoochoose[13,4?????????????????????????????????????????????-50]
Tmall[14,25]
Diginetica[13,19,50]
RetailRocket[19,50]
新闻推荐领域 GLEF 2017[5]
Globo[61-62]
Adressa 16G[63]
音乐推荐领域 Last.FM[39,56]
Nowplaying[1,14]
地点推荐领域 Gowalla[55]
Yelp[66]
Foursquare[68]
影视推荐领域 Movielens-1M[63-64]
Kwai[65]
Tiktok[66]
评价指标 非位置敏感型 Precision[13-14,49]
Recall[39-40,50]
HR[19,27]
位置敏感型 MRR[13,19,49]
NDCG[19,34]
基线模型 传统会话推荐模型 POP[13-14,19]
Item-KNN[13-14,19]
FPMC[13-14,19]
基于RNN的会话推荐模型 GRU4Rec[14,32,40]
NARM[14,44,49]
STAMP[14,40,49]
基于GNN的会话推荐模型 SR-GNN[14,24,40]
GC-SAN[12,21,58]
GCE-GNN[43,58]
Summary of Representative Works in the Evaluation Resource
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