Cross-Domain Recommendation Algorithm Combining Information Bottleneck and Graph Convolution

doi:10.3778/j.issn.1002-8331.2306-0417

Abstract

Abstract: The cross-domain recommendation based on transfer learning can effectively learn the mapping function connecting source domain and target domain, but its performance is still affected by poor representation quality and negative transfer problem, and it can not accurately recommend users of cold start. Therefore, a cross-domain recommendation model (IBGC) combining information bottleneck and graph convolution neural network is proposed. The graph convolutional network is used to aggregate the associated user-user and project-item information. The attention mechanism is used to learn user and item preferences to improve the quality of node feature representation. Considering the information interaction between the two domains, three regularizers are designed to capture intra-domain and cross-domain user-item correlation by using the information bottleneck theory, and overlapping user representations in different domains are aligned to solve the negative transfer problem. Experimentsare conducted on four pairs of public datasets in the Amazon dataset. The model has performed better than the baseline model on the three recommendation performance indicators of MRR, HR@K, and NDCG@K, compared with the optimal comparison baseline model on the four datasets, MRR has improved by an average of 34.36%, HR@10 has improved by an average of 34.94%, and NDCG@10 has improved by an average of 36.83%, which proves the validity of the IBGC model.

Key words: cross-domain recommendation algorithms, user cold-start recommendation, graph convolutional neural networks, information bottleneck theory, network embedding learning, attention mechanism

摘要： 基于迁移学习的跨域推荐可以有效地学习连接源域和目标域的映射函数，但其性能仍然受到表征质量不高和负迁移问题的影响，不能有效地为冷启动用户进行推荐，为此提出了一种融合信息瓶颈与图卷积网络的跨域推荐模型（IBGC）。利用图卷积神经网络聚合有关联的用户-用户和项目-项目信息；利用注意力机制学习用户和项目偏好，以提高节点特征表示质量；考虑到两个领域的信息交互，将重叠用户进行嵌入表示的同时限制特定信息的编码，利用信息瓶颈理论设计了三种正则化器，以捕获域内和跨域用户-项目的相关性，并将不同领域的重叠用户表征对齐以解决负迁移问题。在Amazon数据集中的四对公开数据集上进行实验，实验结果表明该模型在MRR、HR@K和NDCG@K三个推荐性能指标上的表现均优于基线模型，在四对数据集上与最优对比基线模型相比，MRR平均提升34.36%，HR@10平均提升34.94%，NDCG@10平均提升36.83%，证明了IBGC模型的有效性。

WANG Yonggui, HU Pengcheng, SHI Qiwen, ZHAO Yang, ZOU Heyu. Cross-Domain Recommendation Algorithm Combining Information Bottleneck and Graph Convolution[J]. Computer Engineering and Applications, 2024, 60(15): 77-90.

王永贵, 胡鹏程, 时启文, 赵炀, 邹赫宇. 融合信息瓶颈与图卷积的跨域推荐算法[J]. 计算机工程与应用, 2024, 60(15): 77-90.

References

[1] BATMAZ Z, YUREKLI A, BILGE A, et al. A review on deep learning for recommender systems: challenges and remedies[J]. Artificial Intelligence Review, 2019, 52: 1-37.
[2] LIN X, WU J, ZHOU C, et al. Task-adaptive neural process for user cold-start recommendation[C]//Proceedings of the Web Conference 2021, 2021: 1306-1316.
[3] ZHU F, WANG Y, CHEN C, et al. Cross-domain recommendation: challenges, progress, and prospects[J]. arXiv:2103.
01696, 2021.
[4] CHANG W C, WU Y, LIU H, et al. Cross-domain kernel induction for transfer learning[C]//Proceedings of the AAAI Conference on Artificial Intelligence, 2017: 1763-1769.
[5] MAN T, SHEN H, JIN X, et al. Cross-domain recommendation: an embedding and mapping approach[C]//Proceedings of the 26th International Joint Conference on Artificial Intelligence, 2017: 2464-2470.
[6] ZHU Y, GE K, ZHUANG F, et al. Transfer-meta framework for cross-domain recommendation to cold-start users[C]//Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2021: 1813-1817.
[7] SALAH A, TRAN T B, LAUW H. Towards source-aligned variational models for cross-domain recommendation[C]//Proceedings of the 15th ACM Conference on Recommender Systems, 2021: 176-186.
[8] SHI J, WANG Q. Cross-domain variational autoencoder for recommender systems[C]//2019 IEEE 11th International Conference on Advanced Infocomm Technology (ICAIT), 2019: 67-72.
[9] MAI S, ZENG Y, HU H. Multimodal information bottleneck: learning minimal sufficient unimodal and multimodal representations[J]. arXiv:2210.17444, 2022.
[10] ZAIDI A, ESTELLA-AGUERRI I, SHAMAI S. On the information bottleneck problems: models, connections, applications and information theoretic views[J]. Entropy, 2020, 22(2): 151.
[11] HU G, ZHANG Y, YANG Q. Conet: collaborative cross networks for cross-domain recommendation[C]//Proceedings of the 27th ACM International Conference on Information and Knowledge Management, 2018: 667-676.
[12] SHENG X R, ZHAO L, ZHOU G, et al. One model to serve all: star topology adaptive recommender for multi-domain CTR prediction[C]//Proceedings of the 30th ACM International Conference on Information & Knowledge Management, 2021: 4104-4113.
[13] KANG S K, HWANG J, LEE D, et al. Semi-supervised learning for cross-domain recommendation to cold-start users[C]//Proceedings of the 28th ACM International Conference on Information and Knowledge Management, 2019: 1563-1572.
[14] 童小凯, 朱欣娟, 王西汉, 等. 数字文化资源知识图谱多目标跨域推荐方法[J]. 计算机工程与应用, 2023, 59(10): 142-150.
TONG X K, ZHU X J, WANG X H, et al. Knowledge graph multi-target cross-domain recommendation on digital cultural resources[J]. Computer Engineering and Applications, 2023, 59(10): 142-150.
[15] WANG J, LV J. Tag-informed collaborative topic modeling for cross domain recommendations[J]. Knowledge-Based Systems, 2020, 203: 106119.
[16] ZHANG W, ZHANG P, ZHANG B, et al. A collaborative transfer learning framework for cross-domain recommendation[J]. arXiv:2306.16425, 2023.
[17] LIAO X, LIU W, ZHENG X, et al. PPGenCDR: a stable and robust framework for privacy-preserving cross-domain recommendation[J]. arXiv:2305.16163, 2023.
[18] HUAI Z, YANG Y, ZHANG M, et al. M2GNN: metapath and multi-interest aggregated graph neural network for tag-based cross-domain recommendation[J]. arXiv:2304.07911, 2023.
[19] EBESU T, SHEN B, FANG Y. Collaborative memory network for recommendation systems[C]//Proceedings of the 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, 2018: 515-524.
[20] GAO M, CHEN L, HE X, et al. Bine: bipartite network embedding[C]//Proceedings of the 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, 2018: 715-724.
[21] WANG X, HE X, WANG M, et al. Neural graph collaborative filtering[C]//Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2019: 165-174.
[22] ZHAO Z, ZHOU H, LI C, et al. Deepemlan: deep embedding learning for attributed networks[J]. Information Sciences, 2021, 543: 382-397.
[23] 胡新荣, 邓杰文, 罗瑞奇, 等. 基于用户兴趣感知的多关系推荐模型[J]. 计算机工程与应用, 2023, 59(11): 231-240.
HU X R, DENG J W, LUO R Q, et al. Multi-relationship recommendation model based on user interest-aware[J]. Computer Engineering and Applications, 2023, 59(11): 231-240.
[24] TISHBY N, PEREIRA F C, BIALEK W. The information bottleneck method[J]. arXiv preprint physics/0004057, 2000.
[25] GERSHMAN S, GOODMAN N. Amortized inference in probabilistic reasoning[C]//Proceedings of the Annual Meeting of the Cognitive Science Society, 2014.
[26] FEDERICI M, DUTTA A, FORRE P, et al. Learning robust representations via multi-view information bottleneck[J]. arXiv:2002.07017, 2020.
[27] LEE C, VAN DER SCHAAR M. A variational information bottleneck approach to multi-omics data integration[C]//International Conference on Artificial Intelligence and Statistics, 2021: 1513-1521.
[28] WAN Z, ZHANG C, ZHU P, et al. Multi-view information-bottleneck representation learning[C]//Proceedings of the AAAI Conference on Artificial Intelligence, 2021: 10085-10092.
[29] ZHAO C, LI C, FU C. Cross-domain recommendation via preference propagation graph net[C]//Proceedings of the 28th ACM International Conference on Information and Knowledge Management, 2019: 2165-2168.
[30] REN Z, ZHAO L, MA J, et al. Mixed information flow for cross-domain sequential recommendations[J]. ACM Transactions on Knowledge Discovery from Data, 2022, 16(4): 1-32.
[31] CAO J, LIN X, CONG X, et al. DisenCDR: learning disentangled representations for cross-domain recommendation[C]//Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2022: 267-277.
[32] NATARAJAN S, VAIRAVASUNDARAM S, KOTECHA K, et al. CD-SemMF: cross-domain semantic relatedness based matrix factorization model enabled with linked open data for user cold start issue[J]. IEEE Access, 2022, 10: 52955-52970.
[33] POOLE B, OZAIR S, OORD A V D, et al. On variational bounds of mutual information[C]//Proceedings of the International Conference on Machine Learning, 2019: 5171-5180.
[34] CHENG P, HAO W, DAI S, et al. CLUB: a contrastive log-ratio upper bound of mutual information[C]]//Proceedings of the 37th International Conference on Machine Learning, 2020: 1779-1788.