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《山东大学学报(理学版)》 ›› 2024, Vol. 59 ›› Issue (7): 1-26.doi: 10.6040/j.issn.1671-9352.1.2023.043

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基于自监督的预训练在推荐系统中的研究

杨纪元1(),马沐阳1,任鹏杰1,*(),陈竹敏1,任昭春1,辛鑫1,蔡飞2,马军1   

  1. 1. 山东大学计算机科学与技术学院, 山东 青岛 266237
    2. 国防科技大学系统工程学院, 湖南 长沙 410015
  • 收稿日期:2023-10-18 出版日期:2024-07-20 发布日期:2024-07-15
  • 通讯作者: 任鹏杰 E-mail:jiyuan.yang@mail.sdu.edu.cn;renpengjie@sdu.edu.cn
  • 作者简介:杨纪元(1999—)，男，硕士研究生，研究方向为信息检索. E-mail：jiyuan.yang@mail.sdu.edu.cn
  • 基金资助:
    国家自然科学基金资助项目(61902219);国家自然科学基金资助项目(61972234);国家自然科学基金资助项目(62072279);国家自然科学基金资助项目(62102234);国家自然科学基金资助项目(62202271);国家自然科学基金资助项目(62272274);山东省自然科学基金资助项目(ZR2021QF129);山东省自然科学基金资助项目(ZR2022QF004);国家重点研发项目(2020YFB1406704);国家重点研发项目(2022YFC3303004);山东省科技创新重点项目(2019JZZY010129);腾讯犀牛鸟专项研究计划资助项目(JR-WXG2021411)

Research on self-supervised pre-training for recommender systems

Jiyuan YANG1(),Muyang MA1,Pengjie REN1,*(),Zhumin CHEN1,Zhaochun REN1,Xin XIN1,Fei CAI2,Jun MA1   

  1. 1. School of Computer Science and Technology, Shandong University, Qingdao 266237, Shandong, China
    2. School of Systems Engineering, National University of Defense Technology, Changsha 410015, Hunan, China
  • Received:2023-10-18 Online:2024-07-20 Published:2024-07-15
  • Contact: Pengjie REN E-mail:jiyuan.yang@mail.sdu.edu.cn;renpengjie@sdu.edu.cn

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摘要：

近来涌现一批研究工作探讨如何将预训练技术应用在推荐场景下并构造预训练任务，以此提升最终的推荐性能。对现有的基于预训练的推荐模型研究进展进行重点综述；并对不同的预训练方法进行分类和比较，在3个推荐系统基准数据集上对一些代表性模型进行实验和分析，相关的数据集和代码已开源；最后对预训练的推荐模型的未来发展趋势进行总结和展望。

关键词: 推荐系统, 综述, 预训练模型, 自监督学习

Abstract:

Plenty of recent studies explores the application of pre-training techniques within the context of recommendation scenarios and the design of pre-training tasks in order to enhance the overall performance of recommendation. This paper extensively reviews the progress in research of recommendation models based on pre-training, classifies and compares different pre-training methods, and conducts extensive experiments and analyses on some representative models using three benchmark datasets for recommendation systems. The datasets and codes have been made open source. Finally, we summarize and prospect the future development trend of recommendation models based on pre-training.

Key words: recommendation system, survey, pre-training model, self-supervised learning

中图分类号: 

  • TP391

表1

评分预测推荐任务(根据用户之前对物品的评分预测用户对未交互物品的评分)"

用户 物品1 物品2 物品3 物品4
用户1 3.0 4.0 5.0
用户2 1.0 2.0 ？ 3.0
用户3 3.0 4.0

图1

社交推荐任务(利用用户社交关系进行物品推荐)"

图2

序列推荐任务(根据用户的历史物品交互记录预测用户下次可能会点击的物品)"

图3

自监督学习流程"

表2

基于预训练的推荐模型汇总"

行号 自监督方法 推荐任务 模型名称 编码器 自监督辅助任务 侧信息
1 生成式 序列推荐 SASRec[49] Transformer 下个物品预测 无
2 生成式 序列推荐 AsRep[51] Transformer 逆序序列生成 无
3 生成式 序列推荐 GC-SAN[93] GCN & Self-attention layer 下个物品预测 无
4 生成式 序列推荐 SQN-SAC[41] 不限 下个物品预测 无
5 生成式 用户属性预测 Conure[109] TCN 物品推荐和用户属性分类 有
6 生成式 序列推荐 BERT4Rec[50] Transformer 掩码物品预测和下个物品预测 无
7 生成式 评分预测 PMGT[110] Transformer 图结构重构和掩码节点特征预测 有
8 生成式 序列推荐 UPRec[111] BERT 掩码物品预测、用户属性预测和社交关系检测 有
9 生成式 评分预测 U-Bert[112] BERT 掩码评论预测和观点打分预测 有
10 生成式 序列推荐 IERT[113] BERT 下个单词/句子预测 有
11 生成式 序列推荐和用户属性预测 PeterRec[114] NextITNet 下个物品预测 无
12 生成式 用户表征学习 ShopperBERT[28] BERT 下个物品预测 有
13 生成式 物品推荐 CHEST[33] Heterogeneous Subgraph Transformer 掩码节点/边预测和元图类型预测 有
14 生成式 序列推荐 MrTransformer (PE)[25] Transformer 兴趣分离和重构 无
15 对比式 物品推荐 MSSL[115] DNN 剪裁/掩码物品特征 有
16 对比式 物品推荐 CLRec[116] 不限 无 无
17 对比式 序列推荐 CL4SRec[52] Transformer 剪裁/掩码/重新排序物品特征 无
18 对比式 序列推荐 DuoRec[43] Transformer 采样具有相同目标物品的序列作为正例 无
19 对比式 物品推荐 SGL[53] GCN 边/节点随机删除、随机游走 无
20 对比式 社交推荐 SEPT[84] GCN 构建不同视图的图 有
21 对比式 序列推荐 S3-Rec[42] Transformer 学习物品特征、物品、子序列以及序列之间的关系 有
22 对比式 序列推荐 DHCN[54] GCN 构建多视图超图 无
23 对比式 序列推荐 Disentangled[117] Transformer 学习序列中前半部分和后半部分之间的联系 无
24 对比式 社交推荐 S2-MHCN[85] GCN 构建多视图超图 有

图4

自回归式模型(以SASRec[49]为例"

图5

自编码式模型(以BERT4Rec[50]为例)"

图6

基于不同用户交互记录关系的对比"

图7

对比局部与整体关系，最大化用户交互记录的互信息"

表3

本文使用的数据集统计特性"

数据集 用户个数 物品个数 属性个数 交互个数 最小长度 最大长度 平均长度 平均属性 稠密度/%
Amazon-Beauty 22 364 12 102 2 230 194 687 5 50 8.7 3.93 0.07
MovieLens-1M 6 040 3 352 18 269 721 17 50 44.6 1.70 1.33
Yelp 22 845 16 552 1 158 237 004 5 50 10.3 4.92 0.06

表4

在Amazon-Beauty数据集上的实验结果"

Models Recall@5 Recall@10 Recall@20 NDCG@5 NDCG@10 NDCG@20 MRR@5 MRR@10 MRR@20
SASRec 36.58 46.54 58.67 27.91 31.12 34.17 25.79 26.36 27.19
BERT4Rec 39.85 49.20 61.02 30.49 33.50 36.48 27.39 28.62 29.44
S3-Rec 46.15 56.98 68.76 34.53 38.03 41.00 31.94 32.13 32.94
ASRep 37.71 46.97 58.42 29.05 32.04 34.92 26.18 27.41 28.19
CL4SRec 41.45 51.10 62.52 32.34 35.45 38.32 28.48 28.70 30.49
MrTransformer (PE) 42.28 52.10 63.86 32.68 35.85 38.82 29.50 30.81 31.62
MrTransformer 40.15 50.19 62.25 30.80 34.04 37.08 27.70 29.04 29.86
SGL 38.14 49.31 60.63 27.34 30.97 33.82 23.79 25.29 26.07
SGL-p 36.10 47.00 59.09 25.99 29.52 32.57 22.66 24.12 24.95
DHCN 43.62 52.83 63.33 34.26 37.24 39.88 31.17 32.29 33.12
DHCN-p 43.45 52.53 62.82 34.06 37.02 39.59 30.98 32.20 32.90

表5

在Yelp数据集上的实验结果"

Models Recall@5 Recall@10 Recall@20 NDCG@5 NDCG@10 NDCG@20 MRR@5 MRR@10 MRR@20
SASRec 57.66 78.59 94.12 40.57 47.34 51.33 35.19 37.74 38.88
BERT4Rec 63.32 81.81 93.68 45.65 51.64 54.69 39.82 42.30 43.17
S3-Rec 64.24 83.51 96.60 46.15 52.40 55.77 42.54 42.78 43.74
ASReP 63.55 81.17 91.28 45.05 50.77 53.37 38.95 41.33 42.06
CL4SRec 63.78 81.14 92.22 46.44 52.08 54.93 40.71 43.06 43.86
MrTransformer (PE) 64.80 81.23 92.21 47.74 53.07 55.78 42.11 44.31 45.07
MrTransformer 63.97 82.18 93.90 45.99 51.90 54.92 40.05 42.51 43.36
SGL 66.17 82.60 92.23 48.67 54.01 56.49 42.89 45.11 45.82
SGL-p 62.57 80.39 92.20 45.64 51.43 54.46 42.89 45.11 45.82
DHCN 64.04 80.00 94.86 48.15 53.69 57.20 42.93 45.21 46.20
DHCN-p 64.28 82.35 94.68 48.48 54.01 57.42 43.29 45.57 46.53

表6

在MovieLens-1M数据集上的实验结果"

Models Recall@5 Recall@10 Recall@20 NDCG@5 NDCG@10 NDCG@20 MRR@5 MRR@10 MRR@20
SASRec 77.14 87.24 93.26 59.50 62.81 64.36 54.86 55.00 55.45
BERT4Rec 77.83 87.31 93.05 61.12 64.23 65.68 55.55 56.85 57.25
S3-Rec 75.28 86.21 93.03 56.15 59.73 61.46 49.78 51.29 51.76
ASReP 77.98 87.53 93.36 60.87 64.00 64.48 55.16 56.47 56.89
CL4SRec 73.84 84.16 91.54 56.61 59.98 61.86 50.87 52.28 52.80
MrTransformer (PE) 78.34 87.22 93.03 62.05 64.94 66.42 56.62 57.83 58.24
MrTransformer 77.72 85.98 92.28 61.67 64.50 66.19 56.60 57.78 58.26
SGL 61.61 76.82 87.62 43.01 47.92 50.68 36.86 38.89 39.66
SGL-p 59.06 76.29 88.21 40.60 46.19 49.23 34.52 36.83 37.68
DHCN 72.89 84.10 91.69 55.61 59.18 61.13 49.95 51.45 51.99
DHCN-p 72.39 83.37 91.70 55.30 58.97 60.98 49.63 51.16 51.72

表7

不同数据集中不同长度的序列占比统计"

Length Amazon-Beauty MovieLens-1M Yelp
(0, 20] 21 228(94.92%) 177(2.93%) 20 744(90.80%)
(20, 30] 655(2.92%) 684(11.32%) 1 094(4.78%)
(30, 40] 231(1.03%) 543(8.99%) 511(2.23%)
(40, 50] 250(1.11%) 4 636(76.75%) 496(2.17%)

图8

数据集Amazon-Beauty上不同长度序列的Recall@10性能"

图9

数据集Amazon-Beauty上不同长度序列的NDCG@10性能"

图10

数据集MovieLens-1M上不同序列长度在Recall@10上的性能"

图11

数据集MovieLens-1M上不同序列长度在NDCG@10上的性能"

图12

数据集Yelp上不同序列长度在Recall@10上的性能"

图13

数据集Yelp上不同序列长度在NDCG@10上的性能"

表8

在MovieLens-1M数据集上对SGL模型的调参分析"

Models Recall@5 Recall@10 Recall@20 NDCG@5 NDCG@10 NDCG@20 MRR@5 MRR@10 MRR@20
lr=0.001, layers=3, ssl_weight=0.02 61.51 76.87 88.28 39.66 47.92 50.83 36.77 38.85 39.66
lr=0.001, layers=4, ssl_weight=0.02 62.05 76.89 88.03 43.61 48.44 51.27 37.52 39.53 40.31
lr=0.001, layers=5, ssl_weight=0.02 61.71 77.45 87.81 43.17 48.31 50.94 37.06 39.21 39.47
lr=0.001, layers=4, ssl_weight=0.03 61.77 77.15 88.36 43.11 48.12 50.98 36.94 39.03 39.83
lr=0.001, layers=4, ssl_weight=0.04 61.23 77.05 87.67 43.08 48.23 50.95 37.07 39.22 39.98
lr=0.001, layers=4, ssl_weight=0.05 61.18 76.71 87.24 43.19 48.25 50.93 37.24 39.34 40.09
lr=0.002, layers=4, ssl_weight=0.02 61.29 76.77 87.37 43.22 48.27 50.97 37.23 39.34 40.10
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