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曹阳1,2, 高旻1,2(), 余俊良3, 范琪琳1,2, 荣文戈4, 文俊浩1,2
收稿日期:
2021-04-20
修回日期:
2021-06-28
出版日期:
2022-07-18
通讯作者:
作者简介:
基金资助:
CAO Yang1,2, GAO Min1,2(), YU Jun-liang3, FAN Qi-lin1,2, RONG Wen-ge4, WEN Jun-hao1,2
Received:
2021-04-20
Revised:
2021-06-28
Online:
2022-07-18
Corresponding author:
摘要:
近年来,可以有效缓解数据稀疏和冷启动问题的社会化推荐受到了研究者和业界的关注.社会化推荐利用显式或隐式社交关系作为辅助信息,提升了推荐性能.然而,目前的社会化推荐模型通常采用普通图描述社交关系.普通图中的边常描述为成对节点的关系,这种方法适合描述显式关系,但难以描述复杂的隐式关系,如购买过同一商品的多个用户之间的集合关系,因此难以学习到准确的节点表示,影响推荐的性能.针对此问题,本论文结合超图和普通图,提出基于双图混合随机游走的推荐模型(BG-Rec).构建超图描述复杂的隐式关系,同时用普通图描述显式的社交关系,并在两种图上定义混合随机游走策略,生成结合隐式关系和显式关系的游走节点序列,学习更准确的节点嵌入表示.根据用户评分的高低,构建了正反馈超图和负反馈超图,考虑更细粒度的朋友关系,以识别可靠的朋友.融合可靠朋友的偏好和后验概率最大化优化物品个性化排序.三个公开数据集的大量实验表明了BG-Rec在推荐性能上的优越性,冷启动和消融实验表明了其在缓解冷启动问题的有效性和超图建模的合理性.
中图分类号:
曹阳, 高旻, 余俊良, 范琪琳, 荣文戈, 文俊浩. 基于双图混合随机游走的社会化推荐模型[J]. 电子学报, DOI: 10.12263/DZXB.20210504.
CAO Yang, GAO Min, YU Jun-liang, FAN Qi-lin, RONG Wen-ge, WEN Jun-hao. Bi-Graph Mix-random Walk Based Social Recommendation Model[J]. Acta Electronica Sinica, DOI: 10.12263/DZXB.20210504.
Dataset | Users | Items | Feedbacks | Density | Relations |
---|---|---|---|---|---|
LastFM | 1 892 | 17 632 | 92 834 | 0.28% | 25 434 |
Douban | 2 831 | 22 129 | 606 467 | 0.97% | 35 624 |
Epinions | 15 064 | 23 575 | 356 959 | 0.10% | 285 659 |
表1 数据集统计表
Dataset | Users | Items | Feedbacks | Density | Relations |
---|---|---|---|---|---|
LastFM | 1 892 | 17 632 | 92 834 | 0.28% | 25 434 |
Douban | 2 831 | 22 129 | 606 467 | 0.97% | 35 624 |
Epinions | 15 064 | 23 575 | 356 959 | 0.10% | 285 659 |
Dataset | Metric | BPR | SBPR | RSGAN | IF-BPR | LightGCN | SGL | BG-Rec+ | Improve |
---|---|---|---|---|---|---|---|---|---|
LastFM | Prec@10 | 0.042 6 | 0.087 | 0.081 7 | 0.102 1 | 0.096 7 | 0.077 | 0.105 0 | 2.84% |
Rec@10 | 0.064 8 | 0.130 9 | 0.122 4 | 0.152 3 | 0.145 | 0.115 7 | 0.157 7 | 3.55% | |
F1@10 | 0.051 4 | 0.104 5 | 0.097 9 | 0.122 2 | 0.116 | 0.092 5 | 0.126 1 | 3.19% | |
MAP@10 | 0.025 8 | 0.058 7 | 0.054 5 | 0.070 1 | 0.064 5 | 0.052 6 | 0.072 6 | 3.57% | |
NDCG@10 | 0.060 2 | 0.125 7 | 0.118 6 | 0.148 2 | 0.138 3 | 0.113 4 | 0.152 5 | 2.90% | |
Douban | Prec@10 | 0.123 6 | 0.158 8 | 0.173 | 0.175 7 | 0.125 7 | 0.146 2 | 0.182 0 | 3.59% |
Rec@10 | 0.041 3 | 0.055 4 | 0.057 5 | 0.058 3 | 0.047 6 | 0.048 4 | 0.061 5 | 5.49% | |
F1@10 | 0.061 9 | 0.082 2 | 0.086 3 | 0.087 5 | 0.069 | 0.072 7 | 0.092 0 | 5.14% | |
MAP@10 | 0.071 1 | 0.097 2 | 0.108 3 | 0.109 7 | 0.067 2 | 0.084 | 0.115 1 | 4.92% | |
NDCG@10 | 0.140 8 | 0.181 2 | 0.195 5 | 0.197 4 | 0.137 8 | 0.162 1 | 0.205 4 | 4.05% | |
NDCG@10 | 0.140 8 | 0.181 2 | 0.195 5 | 0.197 4 | 0.137 8 | 0.162 1 | 0.205 4 | 4.05% | |
Epinions | Prec@10 | 0.011 | 0.014 5 | 0.018 6 | 0.015 7 | 0.016 7 | 0.019 2 | 0.020 0 | 4.17% |
Rec@10 | 0.025 1 | 0.030 9 | 0.041 5 | 0.034 2 | 0.036 9 | 0.042 4 | 0.044 0 | 3.77% | |
F1@10 | 0.015 3 | 0.019 7 | 0.025 7 | 0.021 5 | 0.023 | 0.026 4 | 0.027 5 | 4.17% | |
MAP@10 | 0.009 | 0.012 2 | 0.016 6 | 0.015 1 | 0.014 8 | 0.018 6 | 0.019 1 | 2.69% | |
NDCG@10 | 0.019 4 | 0.025 3 | 0.033 5 | 0.029 2 | 0.030 1 | 0.035 9 | 0.037 3 | 3.90% | |
NDCG@10 | 0.019 4 | 0.025 3 | 0.033 5 | 0.029 2 | 0.030 1 | 0.035 9 | 0.037 3 | 3.90% |
表2 BG-Rec和其他算法的性能比较
Dataset | Metric | BPR | SBPR | RSGAN | IF-BPR | LightGCN | SGL | BG-Rec+ | Improve |
---|---|---|---|---|---|---|---|---|---|
LastFM | Prec@10 | 0.042 6 | 0.087 | 0.081 7 | 0.102 1 | 0.096 7 | 0.077 | 0.105 0 | 2.84% |
Rec@10 | 0.064 8 | 0.130 9 | 0.122 4 | 0.152 3 | 0.145 | 0.115 7 | 0.157 7 | 3.55% | |
F1@10 | 0.051 4 | 0.104 5 | 0.097 9 | 0.122 2 | 0.116 | 0.092 5 | 0.126 1 | 3.19% | |
MAP@10 | 0.025 8 | 0.058 7 | 0.054 5 | 0.070 1 | 0.064 5 | 0.052 6 | 0.072 6 | 3.57% | |
NDCG@10 | 0.060 2 | 0.125 7 | 0.118 6 | 0.148 2 | 0.138 3 | 0.113 4 | 0.152 5 | 2.90% | |
Douban | Prec@10 | 0.123 6 | 0.158 8 | 0.173 | 0.175 7 | 0.125 7 | 0.146 2 | 0.182 0 | 3.59% |
Rec@10 | 0.041 3 | 0.055 4 | 0.057 5 | 0.058 3 | 0.047 6 | 0.048 4 | 0.061 5 | 5.49% | |
F1@10 | 0.061 9 | 0.082 2 | 0.086 3 | 0.087 5 | 0.069 | 0.072 7 | 0.092 0 | 5.14% | |
MAP@10 | 0.071 1 | 0.097 2 | 0.108 3 | 0.109 7 | 0.067 2 | 0.084 | 0.115 1 | 4.92% | |
NDCG@10 | 0.140 8 | 0.181 2 | 0.195 5 | 0.197 4 | 0.137 8 | 0.162 1 | 0.205 4 | 4.05% | |
NDCG@10 | 0.140 8 | 0.181 2 | 0.195 5 | 0.197 4 | 0.137 8 | 0.162 1 | 0.205 4 | 4.05% | |
Epinions | Prec@10 | 0.011 | 0.014 5 | 0.018 6 | 0.015 7 | 0.016 7 | 0.019 2 | 0.020 0 | 4.17% |
Rec@10 | 0.025 1 | 0.030 9 | 0.041 5 | 0.034 2 | 0.036 9 | 0.042 4 | 0.044 0 | 3.77% | |
F1@10 | 0.015 3 | 0.019 7 | 0.025 7 | 0.021 5 | 0.023 | 0.026 4 | 0.027 5 | 4.17% | |
MAP@10 | 0.009 | 0.012 2 | 0.016 6 | 0.015 1 | 0.014 8 | 0.018 6 | 0.019 1 | 2.69% | |
NDCG@10 | 0.019 4 | 0.025 3 | 0.033 5 | 0.029 2 | 0.030 1 | 0.035 9 | 0.037 3 | 3.90% | |
NDCG@10 | 0.019 4 | 0.025 3 | 0.033 5 | 0.029 2 | 0.030 1 | 0.035 9 | 0.037 3 | 3.90% |
Model | Component | Dataset | Prec@10 | Rec@10 | F1@10 | MAP@10 | NDCG@10 |
---|---|---|---|---|---|---|---|
BG-Recw/oH | Graph√ | LastFM | 0.102 1 | 0.152 3 | 0.122 2 | 0.070 1 | 0.148 2 |
Douban | 0.175 7 | 0.058 3 | 0.087 5 | 0.109 7 | 0.197 4 | ||
Epinions | 0.015 7 | 0.034 2 | 0.021 5 | 0.015 1 | 0.029 2 | ||
BG-Recw/oG | Hypergraph√ | LastFM | 0.100 0 | 0.150 9 | 0.120 3 | 0.068 5 | 0.144 5 |
Douban | 0.152 7 | 0.055 9 | 0.081 9 | 0.092 7 | 0.173 4 | ||
Epinions | 0.010 2 | 0.023 1 | 0.014 2 | 0.009 2 | 0.018 6 | ||
BG-Rec+ | Graph√ | LastFM | 0.105 0 | 0.157 7 | 0.126 1 | 0.072 6 | 0.152 5 |
Douban | 0.182 0 | 0.061 5 | 0.092 0 | 0.115 1 | 0.205 4 | ||
Hypergraph√ | Epinions | 0.020 0 | 0.044 0 | 0.027 5 | 0.019 1 | 0.037 3 |
表3 BG-rec分别消去超图和普通图的对比情况
Model | Component | Dataset | Prec@10 | Rec@10 | F1@10 | MAP@10 | NDCG@10 |
---|---|---|---|---|---|---|---|
BG-Recw/oH | Graph√ | LastFM | 0.102 1 | 0.152 3 | 0.122 2 | 0.070 1 | 0.148 2 |
Douban | 0.175 7 | 0.058 3 | 0.087 5 | 0.109 7 | 0.197 4 | ||
Epinions | 0.015 7 | 0.034 2 | 0.021 5 | 0.015 1 | 0.029 2 | ||
BG-Recw/oG | Hypergraph√ | LastFM | 0.100 0 | 0.150 9 | 0.120 3 | 0.068 5 | 0.144 5 |
Douban | 0.152 7 | 0.055 9 | 0.081 9 | 0.092 7 | 0.173 4 | ||
Epinions | 0.010 2 | 0.023 1 | 0.014 2 | 0.009 2 | 0.018 6 | ||
BG-Rec+ | Graph√ | LastFM | 0.105 0 | 0.157 7 | 0.126 1 | 0.072 6 | 0.152 5 |
Douban | 0.182 0 | 0.061 5 | 0.092 0 | 0.115 1 | 0.205 4 | ||
Hypergraph√ | Epinions | 0.020 0 | 0.044 0 | 0.027 5 | 0.019 1 | 0.037 3 |
1 | YU X, JIANG F, DU J, et al. A cross-domain collaborative filtering algorithm with expanding user and item features via the latent factor space of auxiliary domains [J]. Pattern Recognition, 2019, 94(1): 96-109. |
2 | QI L, WANG X, XU X, et al. Privacy-aware cross-platform service recommendation based on enhanced locality-sensitive hashing [J]. IEEE Transactions on Network Science and Engineering, 2021, 8(2): 1145-1153. |
3 | 李琳,唐守廉.基于多层注意力表示的音乐推荐模型 [J].电子学报, 2020, 48(9): 1672-1679. |
LI L, TANG S L. Hierarchical attention representation model for music recommendation[J]. Acta Electronica Sinica, 2020, 48(9): 1672-1679. (In Chinese) | |
4 | 郭景峰,朱晓松,李爽.直播电视推荐系统的评分预测算法研究 [J]. 电子学报, 2020, 48(9): 1735-1740. |
GUO J F, ZHU X S, LI S. Research on rating prediction algorithm of live TV recommender systems[J]. Acta Electronica Sinica, 2020, 48(9): 1735-1740. (in Chinese) | |
5 | CIALDINI R B, GOLDSTEIN N J. Social influence: Compliance and conformity [J]. Annu Rev Psychol, 2004, 55(1): 591-621. |
6 | SINHA R R, SWEARINGEN K. Comparing recommendations made by online systems and friends[C]//Proceedings of the Second DELOS Network of Excellence Workshop on Personalisation and Recommender Systems in Digital Libraries. Sophia Antipolis: ERCIM, 2001: 1-6. |
7 | DOU K, GUO B, KUANG L. A privacy-preserving multimedia recommendation in the context of social network based on weighted noise injection [J]. Multimedia Tools and Applications, 2019, 78(19): 26907-26926. |
8 | ZHAO T, MCAULEY J, KING I. Leveraging social connections to improve personalized ranking for collaborative filtering [C]//Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management. New York: ACM, 2014: 261-270. |
9 | MA H, KING I, LYU M R. Learning to recommend with social trust ensemble[C]//Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2009: 203-210. |
10 | KANG M, BI Y, WU Z, et al. A heterogeneous conversational recommender system for financial products[C]//Proceedings of the 28th ACM International Conference on Information and Knowledge Management. New York: ACM, 2019: 26-30. |
11 | ZHAO H, ZHOU Y, SONG Y, et al. Motif enhanced recommendation over heterogeneous information network[C]//Proceedings of the 28th ACM International Conference on Information and Knowledge Management. New York: ACM, 2019: 2189-2192. |
12 | YU J, GAO M, LI J, et al. Adaptive implicit friends identification over heterogeneous network for social recommendation[C]//Proceedings of the 27th ACM International Conference on Information and Knowledge Management. New York: ACM, 2018: 357-366. |
13 | YU J, YIN H, LI J, et al. Self-supervised multi-channel hypergraph convolutional network for social recommendation[C]//The Web Conference 2021. New York: ACM, 2021: 413-424. |
14 | JI S, FENG Y, JI R, et al. Dual channel hypergraph collaborative filtering[C]//Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. New York: ACM, 2020: 2020-2029. |
15 | MA H, YANG H, LYU M R, et al. Sorec: Social recommendation using probabilistic matrix factorization[C]//Proceedings of the 17th ACM Conference on Information and Knowledge Management. New York: ACM, 2008: 931-940. |
16 | YANG B, LEI Y, LIU J, et al. Social collaborative filtering by trust [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 39(8): 1633-1647. |
17 | CHEN J, FENG Y, ESTER M, et al. Modeling users' exposure with social knowledge influence and consumption influence for recommendation[C]//Proceedings of the 27th ACM International Conference on Information and Knowledge Management. New York: ACM, 2018: 953-962. |
18 | RENDLE S, FREUDENTHALER C, GANTNER Z, et al. BPR: Bayesian personalized ranking from implicit feedback[C]//In Proceedings of the Twenty-fifth Conference on Uncertainty in Artificial Intelligence. Arlington: AUAI Press, 2009: 452-461 |
19 | TANG J, HU X, LIU H. Social recommendation: a review [J]. Social Network Analysis and Mining, 2013, 3(4): 1113-1133. |
20 | WANG X, HOI S C H, ESTER M, et al. Learning personalized preference of strong and weak ties for social recommendation[C]//Proceedings of the 26th International Conference on World Wide Web. Geneva: IW 3C2, 2017: 1601-1610. |
21 | GOYAL P, FERRARA E. Graph embedding techniques, applications, and performance: A survey [J]. Knowledge-Based Systems, 2018, 151(1): 78-94. |
22 | FAN W, MA Y, LI Q, et al. Graph neural networks for social recommendation[C]//Proceedings of the Web Conference 2019. New York: ACM, 2019: 417-426. |
23 | WU L, SUN P, FU Y, et al. A neural influence diffusion model for social recommendation[C]//Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2019: 235-244. |
24 | YU J, GAO M, YIN H, et al. Generating reliable friends via adversarial training to improve social recommendation[C]//2019 IEEE International Conference on Data Mining. New York: IEEE, 2019: 768-777. |
25 | YU J, YIN H, LI J, et al. Enhance social recommendation with adversarial graph convolutional networks [EB/OL]. (2020-10-23)[2021-06-28]. . |
26 | CHITRA U, RAPHAEL B J. Random walks on hypergraphs with edge-dependent vertex weights[C]//Proceedings of the 36th International Conference on Machine Learning. Long Beach: IMLS, 2019: 2002-2011. |
27 | FROLOV E, OSELEDETS I. Fifty shades of ratings: How to benefit from a negative feedback in top-N recommendations tasks[C]//Proceedings of the 10th ACM Conference on Recommender Systems. New York: ACM, 2016: 91-98. |
28 | DONG Y, CHAWLA N V, SWAMI A. Metapath2vec: Scalable representation learning for heterogeneous networks[C]//Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2017: 135-144. |
29 | MIKOLOV T, SUTSKEVER I, CHEN K, et al. Distributed representations of words and phrases and their compositionality [J]. Advances in Neural Information Processing Systems, 2013, 26(1): 3111-3119. |
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 | WU J, WANG X, FENG F, et al. Self-supervised graph learning for recommendation[C]//Proceedings of the 44rd International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2021: 639-648. |
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