基于图共现增强多层感知机的会话推荐

doi:10.11772/j.issn.1001-9081.2023081063

《计算机应用》唯一官方网站 ›› 2024, Vol. 44 ›› Issue (8): 2357-2364.DOI: 10.11772/j.issn.1001-9081.2023081063

基于图共现增强多层感知机的会话推荐

唐廷杰¹^,², 黄佳进³(), 秦进¹^,², 陆辉¹^,²

^1.公共大数据国家重点实验室(贵州大学), 贵阳 550025
^2.贵州大学计算机科学与技术学院, 贵阳 550025
^3.北京工业大学信息学部, 北京 100124

收稿日期:2023-08-07 修回日期:2023-10-10 接受日期:2023-10-17 发布日期:2023-12-18 出版日期:2024-08-10
通讯作者: 黄佳进
作者简介:唐廷杰（1999—），男，贵州黔东南人，硕士研究生，主要研究方向：推荐系统
黄佳进（1977—），男，贵州遵义人，助理研究员，博士，主要研究方向：推荐系统 jhuang@bjut.edu.cn
秦进（1978—），男，贵州毕节人，副教授，博士，主要研究方向：强化学习、计算智能
陆辉（1998—），男，贵州安顺人，硕士研究生，主要研究方向：动态聚类。
基金资助:
贵州省科技计划项目(黔科合基础［2020］1Y275)

Session-based recommendation based on graph co-occurrence enhanced multi-layer perceptron

Tingjie TANG¹^,², Jiajin HUANG³(), Jin QIN¹^,², Hui LU¹^,²

^1.State Key Laboratory of Public Big Data （Guizhou University），Guiyang Guizhou 550025，China
^2.College of Computer Science and Technology，Guizhou University，Guiyang Guizhou 550025，China
^3.Faculty of Information Technology，Beijing University of Technology，Beijing 100124，China

Received:2023-08-07 Revised:2023-10-10 Accepted:2023-10-17 Online:2023-12-18 Published:2024-08-10
Contact: Jiajin HUANG
About author:TANG Tingjie ， born in 1999， M. S. candidate. His researchinterests include recommender system.
HUANG Jiajin ， born in 1977， Ph. D.， assistant research fellow.His research interests include recommender system.
QIN Jin ， born in 1978， Ph. D.， associate professor. His researchinterests include reinforcement learning， computational intelligence.
LU Hui， born in 1998， M. S. candidate. His research interestsinclude dynamic clustering.
Supported by:
This work is partially supported by Guizhou Science and TechnologyPlan Project（ Qiankehe Fund［2020］1Y275）.

摘要/Abstract

摘要：

针对多层感知机（MLP）架构无法捕获会话序列上下文中的共现关系的问题，提出了一种基于图共现增强MLP的会话推荐模型GCE-MLP。首先，利用MLP架构捕获会话序列的顺序依赖关系，同时通过共现关系学习层获得序列上下文中的共现关系，并通过信息融合模块得到会话表示；其次，设计了特定的特征选择层，旨在扩大不同关系学习层输入特征的差异性；最后，通过噪声对比任务最大化两种关系表征之间的互信息，进一步增强对会话兴趣的表征学习。在多个真实数据集上的实验结果表明GCE-MLP的推荐性能优于目前主流的模型，验证了该模型的有效性。与最优的MLP架构模型FMLP-Rec（Filter-enhanced MLP for Recommendation）相比，在Diginetica数据集上，P@20最高达到了54.08%，MRR@20最高达到了18.87%，分别提升了2.14和1.43个百分点；在Yoochoose数据集上，P@20最高达到了71.77%，MRR@20最高达到了31.78%，分别提升了0.48和1.77个百分点。

关键词: 推荐系统, 会话推荐, 多层感知机, 共现关系, 表征学习

Abstract:

Aiming at the problem that the Multi-Layer Perceptron （MLP） architecture can not capture the co-occurrence relationship in the context of session sequence， a session-based recommendation model based on Graph Co-occurrence Enhanced MLP （GCE-MLP） was proposed. Firstly， the sequential dependency of the session sequence was captured by the MLP architecture， and at the same time， the co-occurrence relationship in the sequence context was obtained through the co-occurrence relationship learning layer， and the session representation was obtained through the information fusion module. Secondly， a specific feature selection layer was designed to amplify the diversity of input features of different relation learning layers. Finally， the representation learning of sessional interest was further enhanced by maximizing the mutual information between two relational representations via a noise contrastive task. Experimental results on multiple real datasets show that the recommendation performance of the GCE-MLP is better than those of the current mainstream models， which verifies the effectiveness of GCE-MLP. Compared with the optimal MLP architecture model FMLP-Rec（Filter-enhanced MLP for Recommendation）， GCE-MLP achieves the P@20 of 54.08% and the MRR@20 of 18.87% for Diginetica dataset， which are respectively increased by about 2.14 and 1.43 percentage points； GCE-MLP achieves the P@20 of 71.77% and the MRR@20 of 31.78% for Yoochoose dataset， which are respectively increased by about 0.48 and 1.77 percentage points.

Key words: recommendation system, session-based recommendation, Multi-Layer Perceptron (MLP), co-occurrence relationship, representation learning

中图分类号:

TP391.1

唐廷杰, 黄佳进, 秦进, 陆辉. 基于图共现增强多层感知机的会话推荐[J]. 计算机应用, 2024, 44(8): 2357-2364.

Tingjie TANG, Jiajin HUANG, Jin QIN, Hui LU. Session-based recommendation based on graph co-occurrence enhanced multi-layer perceptron[J]. Journal of Computer Applications, 2024, 44(8): 2357-2364.

图/表 9

参考文献 31

1	HE X， LIAO L， ZHANG H， et al. Neural collaborative filtering［C］// Proceedings of the 26th International Conference on World Wide Web. Republic and Canton of Geneva， Switzerland： International World Wide Web Conferences Steering Committee， 2017： 173-182.
2	ZHANG S， YAO L， SUN A， et al. Deep learning based recommender system： a survey and new perspectives ［J］. ACM Computing Surveys， 2019， 52（1）： 5.
3	WANG S， CAO L， WANG Y， et al. A survey on session-based recommender systems ［J］. ACM Computing Surveys， 2021， 54（7）： 154.
4	HIDASI B， KARATZOGLOU A， BALTRUNAS L， et al. Session-based recommendations with recurrent neural networks［EB/OL］.（2015-11-21）［2023-08-05］. .
5	LI J， REN P， CHEN Z， et al. Neural attentive session-based recommendation ［C］// Proceedings of the 2017 ACM Conference on Information and Knowledge Management. New York： ACM， 2017： 1419-1428.
6	TAN Y K， XU X， LIU Y. Improved recurrent neural networks for session-based recommendations ［C］// Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. New York： ACM， 2016： 17-22.
7	HIDASI B， KARATZOGLOU A. Recurrent neural networks with top-k gains for session-based recommendations ［C］// Proceedings of the 27th ACM International Conference on Information and Knowledge Management. New York： ACM， 2018： 843-852.
8	LIU Q， ZENG Y， MOKHOSI R， et al. STAMP： short-term attention/memory priority model for session-based recommendation ［C］// Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. New York： ACM， 2018： 1831-1839.
9	WU S， TANG Y， ZHU Y， et al. Session-based recommendation with graph neural networks ［C］// Proceedings of the 32rd AAAI Conference on Artificial Intelligence. Menlo Park：AAAI Press， 2019： 346-353.
10	YU F， ZHU Y， LIU Q， et al. TAGNN： target attentive graph neural networks for session-based recommendation ［C］// Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. New York： ACM， 2020： 1921-1924.
11	CHEN Y-H， HUANG L， WANG C-D， et al. Hybrid-order gated graph neural network for session-based recommendation ［J］. IEEE Transactions on Industrial Informatics， 2021， 18（3）： 1458-1467.
12	南宁，杨程屹，武志昊.基于多图神经网络的会话感知推荐模型［J］. 计算机应用，2021， 41（2）： 330-336.
	NAN N， YANG C Y， WU Z H. Multi-graph neural network-based session perception recommendation model［J］. Journal of Computer Applications， 2021， 41（2）： 330-336.
13	任俊伟，曾诚，肖丝雨，等.基于会话的多粒度图神经网络推荐模型［J］.计算机应用，2021， 41（11）： 3164-3170.
	REN J W， ZENG C， XIAO S Y， et al. Session-based recommendation model of multi-granular graph neural network［J］. Journal of Computer Applications， 2021， 41（11）： 3164-3170.
14	PANG Y， WU L， SHEN Q， et al. Heterogeneous global graph neural networks for personalized session-based recommendation ［C］// Proceedings of the 15th ACM International Conference on Web Search and Data Mining. New York： ACM， 2022： 775-783.
15	SHENG Z， ZHANG T， ZHANG Y， et al. Enhanced graph neural network for session-based recommendation ［J］. Expert Systems with Applications， 2023， 213： 118887.
16	LI A， CHENG Z， LIU F， et al. Disentangled graph neural networks for session-based recommendation ［J］. IEEE Transactions on Knowledge and Data Engineering， 2023， 35（8）： 7870-7882.
17	XU C， ZHAO P， LIU Y， et al. Graph contextualized self-attention network for session-based recommendation ［C］// Proceedings of the 28th International Joint Conference on Artificial Intelligence， San Francisco： IJCAI， 2019： 3940-3946.
18	TOLSTIKHIN I O， HOULSBY N， KOLESNIKOV A， et al. MLP-Mixer： an all-MLP architecture for vision ［EB/OL］. （2021-05-04）［2023-08-05］. .
19	TOUVRON H， BOJANOWSKI P， CARON M， et al. ResMLP： feedforward networks for image classification with data-efficient training ［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence， 2023， 45（4）： 5314-5321.
20	ZHOU K， YU H， ZHAO W X， et al. Filter-enhanced MLP is all you need for sequential recommendation ［C］// Proceedings of the 2022 ACM Web Conference. New York： ACM， 2022： 2388-2399.
21	SARWAR B， KARYPIS G， KONSTAN J， et al. Item-based collaborative filtering recommendation algorithms ［C］// Proceedings of the 10th International Conference on World Wide Web. New York： ACM， 2001： 285-295.
22	RENDLE S， FREUDENTHALER C， SCHMIDT-THIEME L. Factorizing personalized Markov chains for next-basket recommendation ［C］// Proceedings of the 19th International Conference on World Wide Web. New York： ACM， 2010： 811-820.
23	WANG Z， WEI W， CONG G， et al. Global context enhanced graph neural networks for session-based recommendation ［C］// Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. New York： ACM， 2020： 169-178.
24	WU J， WANG X， FENG F， et al. Self-supervised graph learning for recommendation ［C］// Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York： ACM， 2021： 726-735.
25	YU J， YIN H， XIA X， et al. Are graph augmentations necessary？ Simple graph contrastive learning for recommendation ［C］// Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York： ACM， 2022： 1294-1303.
26	LI H， LUO X， YU Q， et al. Session-based recommendation via contrastive learning on heterogeneous graph ［C］// Proceedings of the 2021 IEEE International Conference on Big Data. Piscataway： IEEE， 2021： 1077-1082.
27	XIA X， YIN H， YU J， et al. Self-supervised hypergraph convolutional networks for session-based recommendation ［J］. Proceedings of the AAAI Conference on Artificial Intelligence， 2021， 35（5）： 4503-4511.
28	WANG F， LU X， LYU L. CGSNet： contrastive graph self-attention network for session-based recommendation ［J］. Knowledge-Based Systems， 2022， 251： 109282.
29	CHEN T， KORNBLITH S， NOROUZI M， et al. A simple framework for contrastive learning of visual representations ［C］// Proceedings of the 37th International Conference on Machine Learning. New York： JMLR.org， 2020： 1597-1607.
30	HE X， DENG K， WANG X， et al. LightGCN： simplifying and powering graph convolution network for recommendation ［C］// Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. New York： ACM， 2020： 639-648.
31	ZANGERLE E， PICHL M， GASSLER W， et al. # nowplaying music dataset： extracting listening behavior from Twitter ［C］// Proceedings of the 1st International Workshop on Internet-Scale Multimedia Management. New York： ACM， 2014： 21-26.

方法	Diginetica		Yoochoose 1/64		Nowplaying
方法	P@20	MRR@20	P@20	MRR@20	P@20	MRR@20
POP	0.89	0.20	6.71	1.65	2.28	0.86
Item-KNN	35.75	11.57	51.60	21.81	15.94	4.91
FPMC	22.53	6.95	45.62	15.01	7.36	2.82
GRU4REC	29.45	8.33	60.64	22.89	7.92	4.48
NARM	49.70	16.17	68.32	28.63	18.59	6.93
STAMP	45.64	14.32	68.74	29.67	17.66	6.88
SR-GNN	51.26	17.66	70.57	30.94	17.76	7.49
TAGNN	51.53	17.90	71.02	31.12	19.02	7.82
S²-DHCN	53.66	18.51	70.74	30.16	23.50	8.18
Disen-GNN	53.79	18.99	71.46	31.36	22.22	8.22
FMLP-Rec	51.94	17.44	71.29	30.01	22.72	8.12
GCE-MLP	54.08	18.87	71.77	31.78	22.26	8.55

方法	Diginetica		Yoochoose 1/64		Nowplaying
方法	P@20	MRR@20	P@20	MRR@20	P@20	MRR@20
POP	0.89	0.20	6.71	1.65	2.28	0.86
Item-KNN	35.75	11.57	51.60	21.81	15.94	4.91
FPMC	22.53	6.95	45.62	15.01	7.36	2.82
GRU4REC	29.45	8.33	60.64	22.89	7.92	4.48
NARM	49.70	16.17	68.32	28.63	18.59	6.93
STAMP	45.64	14.32	68.74	29.67	17.66	6.88
SR-GNN	51.26	17.66	70.57	30.94	17.76	7.49
TAGNN	51.53	17.90	71.02	31.12	19.02	7.82
S²-DHCN	53.66	18.51	70.74	30.16	23.50	8.18
Disen-GNN	53.79	18.99	71.46	31.36	22.22	8.22
FMLP-Rec	51.94	17.44	71.29	30.01	22.72	8.12
GCE-MLP	54.08	18.87	71.77	31.78	22.26	8.55

变体模型	Diginetica		Yoochoose 1/64		Nowplaying
变体模型	P@20	MRR@20	P@20	MRR@20	P@20	MRR@20
GCE-MLP-C	53.59	18.71	71.42	31.50	22.03	8.12
GCE-MLP-NCL	53.14	18.60	71.20	31.54	22.15	7.94
GCE-MLP-SMG	53.58	18.61	71.68	30.50	22.20	7.66
GCE-MLP-N	53.79	18.67	71.44	31.54	21.98	8.48
GCE-MLP	54.08	18.87	71.77	31.78	22.26	8.55

变体模型	Diginetica		Yoochoose 1/64		Nowplaying
变体模型	P@20	MRR@20	P@20	MRR@20	P@20	MRR@20
GCE-MLP-C	53.59	18.71	71.42	31.50	22.03	8.12
GCE-MLP-NCL	53.14	18.60	71.20	31.54	22.15	7.94
GCE-MLP-SMG	53.58	18.61	71.68	30.50	22.20	7.66
GCE-MLP-N	53.79	18.67	71.44	31.54	21.98	8.48
GCE-MLP	54.08	18.87	71.77	31.78	22.26	8.55

方法	Diginetica				Yoochoose 1/64				Nowplaying
	Short		Long		Short		Long		Short		Long
	P@20	MRR@20	P@20	MRR@20	P@20	MRR@20	P@20	MRR@20	P@20	MRR@20	P@20	MRR@20
Disen-GNN	54.45	19.69	51.50	16.74	72.90	33.60	67.32	26.16	20.96	7.62	23.57	8.84
FMLP-Rec	53.21	18.64	49.33	15.01	72.99	32.46	67.86	25.33	21.82	7.84	24.52	8.74
GCE-MLP-C	54.51	19.46	50.78	16.33	72.99	33.72	67.73	26.31	21.43	7.79	24.26	8.72
GCE-MLP-NCL	54.03	19.40	50.21	16.30	72.65	33.72	67.80	26.64	21.45	7.46	24.15	8.56
GCE-MLP	54.84	19.57	51.69	16.61	73.24	33.86	68.31	26.91	21.04	8.05	24.46	9.46

基于图共现增强多层感知机的会话推荐

Session-based recommendation based on graph co-occurrence enhanced multi-layer perceptron

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