Enhancing Recommender Systems: A Comprehensive Study from Effectivenss to Fairness

In the age of information overload, recommender systems play a crucial role in helping users discover relevant and personalized content in various areas, such as e-commerce, entertainment, and social media. In this thesis, we study how to improve the effectiveness and fairness of existing recommender systems. Specifically, we propose new techniques for the effectiveness of meta-path-based and multi-domain recommendations. Furthermore, we propose a new framework to handle the popularity bias issue to improve the fairness of recommender systems. First, we investigate meta-path-based recommender systems (MPRs) and how to automatically find effective meta-paths as input to these MPRs. We observe that the performance of MPRs is highly sensitive to the meta-paths they use, but existing works manually select the meta-paths from many possible ones. Thus, we propose the Reinforcementlearning-based Meta-path Selection (RMS) framework to discover effective meta-paths automatically. We also propose a new MPR called RMS- HRec, which uses an attention mechanism to aggregate information from the meta-paths. We conduct extensive experiments on real datasets. Compared with the manually selected meta-paths, the meta-paths identified by RMS consistently improve recommendation quality, and RMS-HRec outperforms state-of-the-art recommender systems in terms of recommendation accuracy. Second, we study how to conduct more effective multi-domain recrecommendation (MDR). We find that existing MDR models are difficult to disentangle knowledge that generalizes across domains (e.g., a user likes cheap items) and knowledge specific to a single domain (e.g., a user likes blue clothing but not blue cars) due to their model-level disentangling mechanism. Therefore, we propose an embedding disentangling recommender focusing on disentangling at the embedding level. We also find that existing MDR methods have limited ability to transfer knowledge across domains with small overlaps, so we propose a random walk-based domain alignment strategy to identify similar users/items from different domains, which further helps knowledge sharing. Extensive experiments show that our method consistently outperforms the baselines on all datasets and domains. Finally, we focus on the popularity bias issue in recommender systems, which is important to the fairness of recommendations. Popularity bias is the phenomenon that popular items are recommended much more frequently than they should be. This goes against the goal of providing personalized recommendations and harms user experience and recommendation accuracy. Therefore, we aim to increase the personalization of recommendations to handle it. We find that existing debiasing methods overlook that popularity is defined in a "global" manner (thus, we call it global popularity (GP) in this thesis), which is defined as the proportion of all users who have interacted with the item. Hence, we propose "Personal popularity" (PP), which considers the interests of individual users. Furthermore, we propose the Personal Popularity Aware Counterfactual (PPAC) framework that integrates PP and GP to handle popularity bias. Experiment results show that our method can effectively reduce the recommendations of popular items, enhancing the fairness of recommendations.

[1] H. Abdollahpouri, R. Burke, and B. Mobasher. “Controlling Popularity Bias in Learning-to-Rank Recommendation”. In: RecSys. 2017, pp. 42–46.
[2] H. Abdollahpouri, R. Burke, and B. Mobasher. “Managing Popularity Bias in Recommender Systems with Personalized Re-Ranking”. In: FLAIRS. 2019, pp. 413–418.
[3] H. Abdollahpouri, M. Mansoury, R. Burke, and B. Mobasher. “The Connection Between Popularity Bias, Calibration, and Fairness in Recommendation”. In: RecSys. 2020, pp. 726–731.
[4] H. Abdollahpouri, M. Mansoury, R. Burke, B. Mobasher, and E. C. Malthouse. “User-centered Evaluation of Popularity Bias in Recommender Systems”. In: UMAP. 2021, pp. 119–129.
[5] Q. Ai, V. Azizi, X. Chen, and Y. Zhang. “Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation”. In: Algorithms 11.9 (2018), p. 137.
[6] Amazon. Deep Graph Library. "https://dgl.ai".
[7] S. An, J. Kim, M. Kim, and J. Park. “No Task Left Behind: MultiTask Learning of Knowledge Tracing and Option Tracing for Better Student Assessment”. In: AAAI. 2022, pp. 4424–4431.
[8] V. W. Anelli, T. D. Noia, E. D. Sciascio, A. Ragone, and J. Trotta. “Local Popularity and Time in top-N Recommendation”. In: ECIR. Vol. 11437. 2019, pp. 861–868.
[9] A. Ariza-Casabona, B. Twardowski, and T. K. Wijaya. “Exploiting Graph Structured Cross-Domain Representation for MultiDomain Recommendation”. In: CoRR abs/2302.05990 (2023).
[10] G. Balloccu, L. Boratto, G. Fenu, and M. Marras. “Post Processing Recommender Systems with Knowledge Graphs for Recency, Popularity, and Diversity of Explanations”. In: SIGIR. 2022, pp. 646– 656.
[11] S. Bonner and F. Vasile. “Causal embeddings for recommendation”. In: RecSys. 2018, pp. 104–112.
[12] A. Bordes, N. Usunier, A. Garcia-Duran, J. Weston, and O. Yakhnenko. “Translating embeddings for modeling multi-relational data”. In: NeurIPS 26 (2013).
[13] L. Bottou, J. Peters, J. Q. Candela, D. X. Charles, M. Chickering, E. Portugaly, D. Ray, P. Y. Simard, and E. Snelson. “Counterfactual reasoning and learning systems: the example of computational advertising”. In: JMLR 14.1 (2013), pp. 3207–3260.
[14] R. Cañamares and P. Castells. “A Probabilistic Reformulation of Memory-Based Collaborative Filtering: Implications on Popularity Biases”. In: SIGIR. 2017, pp. 215–224.
[15] J. Cao, X. Lin, X. Cong, J. Ya, T. Liu, and B. Wang. “DisenCDR: Learning Disentangled Representations for Cross-Domain Recommendation”. In: SIGIR. 2022, pp. 267–277.3
[16] J. Cao, J. Sheng, X. Cong, T. Liu, and B. Wang. “Cross-Domain Recommendation to Cold-Start Users via Variational Information Bottleneck”. In: ICDE. 2022, pp. 2209–2223.
[17] A. J. B. Chaney, B. M. Stewart, and B. E. Engelhardt. “How algorithmic confounding in recommendation systems increases homogeneity and decreases utility”. In: RecSys. 2018, pp. 224–232.
[18] H. Chen, X. Wang, R. Xie, Y. Zhou, and W. Zhu. “Cross-domain Recommendation with Behavioral Importance Perception”. In: WWW. 2023, pp. 1294–1304.
[19] J. Chen, X. Wang, F. Feng, and X. He. “Bias Issues and Solutions in Recommender System: Tutorial on the RecSys 2021”. In: RecSys. 2021, pp. 825–827.
[20] L. Chen, F. Yuan, J. Yang, X. He, C. Li, and M. Yang. “User-Specific Adaptive Fine-Tuning for Cross-Domain Recommendations”. In: TKDE 35.3 (2023), pp. 3239–3252.
[21] L. Chen, H. Zhang, J. Xiao, X. He, S. Pu, and S. Chang. “Counterfactual Critic Multi-Agent Training for Scene Graph Generation”. In: ICCV. 2019, pp. 4612–4622.
[22] X. Chen, Y. Zhang, I. W. Tsang, Y. Pan, and J. Su. “Toward Equivalent Transformation of User Preferences in Cross-Domain Recommendation”. In: TIS 41.1 (2023), 14:1–14:31.
[23] Y. Chen, Y. Wang, Y. Ni, A. Zeng, and L. Lin. “Scenario-aware and Mutual-based approach for Multi-scenario Recommendation in E-Commerce”. In: ICDM Workshops. 2020, pp. 127–135.y
[24] Z. Chen, J. Wu, C. Li, J. Chen, R. Xiao, and B. Zhao. “Co-training Disentangled Domain Adaptation Network for Leveraging Popularity Bias in Recommenders”. In: SIGIR. 2022, pp. 60–69.
[25] Z. Chen, R. Xiao, C. Li, G. Ye, H. Sun, and H. Deng. “ESAM: Discriminative Domain Adaptation with Non-Displayed Items to Improve Long-Tail Performance”. In: SIGIR. 2020, pp. 579–588.
[26] T. Domhan, J. T. Springenberg, and F. Hutter. “Speeding Up Automatic Hyperparameter Optimization of Deep Neural Networks by Extrapolation of Learning Curves”. In: IJCAI. AAAI Press, 2015, pp. 3460–3468.
[27] Y. Dong, N. V. Chawla, and A. Swami. “metapath2vec: Scalable Representation Learning for Heterogeneous Networks”. In: SIGKDD. ACM, 2017, pp. 135–144.
[28] S. Fan, J. Zhu, X. Han, C. Shi, L. Hu, B. Ma, and Y. Li. “Metapathguided Heterogeneous Graph Neural Network for Intent Recommendation”. In: SIGKDD. ACM, 2019, pp. 2478–2486.
[29] Y. Fang, Y. Yang, W. Zhang, X. Lin, and X. Cao. “Effective and efficient community search over large heterogeneous information networks”. In: PVLDB 13.6 (2020), pp. 854–867.
[30] F. Feng, W. Huang, X. He, X. Xin, Q. Wang, and T. Chua. “Should Graph Convolution Trust Neighbors? A Simple Causal Inference Method”. In: SIGIR. 2021, pp. 1208–1218.
[31] F. Feng, J. Zhang, X. He, H. Zhang, and T. Chua. “Empowering Language Understanding with Counterfactual Reasoning”. In: ACL/IJCNLP. Vol. ACL/IJCNLP 2021. Findings of ACL. 2021, pp. 2226–2236.5
[32] X. Fu, J. Zhang, Z. Meng, and I. King. “MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding”. In: WWW. 2020.
[33] Z. Fu, Y. Xian, S. Geng, G. de Melo, and Y. Zhang. “Popcorn: Human-in-the-loop Popularity Debiasing in Conversational Recommender Systems”. In: CIKM. 2021, pp. 494–503.
[34] A. Gionis, P. Indyk, and R. Motwani. “Similarity Search in High Dimensions via Hashing”. In: VLDB. 1999, pp. 518–529.
[35] N. Goyal, R. Paneri, A. Agarwal, U. Kalani, A. Sancheti, and N. Chhaya. “CaM-Gen: Causally Aware Metric-Guided Text Generation”. In: ACL. 2022, pp. 2047–2060.
[36] A. Gruson, P. Chandar, C. Charbuillet, J. McInerney, S. Hansen, D. Tardieu, and B. Carterette. “Offline Evaluation to Make Decisions About Playlist Recommendation Algorithms”. In: WSDM. ACM, 2019, pp. 420–428.
[37] Q. Guo, F. Zhuang, C. Qin, H. Zhu, X. Xie, H. Xiong, and Q. He. “A survey on knowledge graph-based recommender systems”. In: TKDE (2020).
[38] P. Gupta, A. Sharma, P. Malhotra, L. Vig, and G. Shroff. “CauSeR: Causal Session-based Recommendations for Handling Popularity Bias”. In: CIKM. 2021, pp. 3048–3052.
[39] Z. Han, F. Xu, J. Shi, Y. Shang, H. Ma, P. Hui, and Y. Li. “Genetic Meta-Structure Search for Recommendation on Heterogeneous Information Network”. In: CIKM. 2020, pp. 455–464.
[40] Z. Han, M. U. Anwaar, S. Arumugaswamy, T. Weber, T. Qiu, H. Shen, Y. Liu, and M. Kleinsteuber. “Metapathand Entityware eyGraph Neural Network for Recommendation”. In: CoRR abs/2010.11793(2020).
[41] Z. Han, X. Zheng, C. Chen, W. Cheng, and Y. Yao. “Intra and Inter Domain HyperGraph Convolutional Network for Cross-Domain Recommendation”. In: WWW. 2023, pp. 449–459.
[42] Q. Hao, Q. Xu, Z. Yang, and Q. Huang. “Learning Unified Embeddings for Recommendation via Meta-path Semantics”. In: MM. ACM, 2021, pp. 3909–3917.
[43] X. Hao, Y. Liu, R. Xie, K. Ge, L. Tang, X. Zhang, and L. Lin. “Adversarial Feature Translation for Multi-domain Recommendation”. In: SIGKDD. 2021, pp. 2964–2973.
[44] R. He, A. Ravula, B. Kanagal, and J. Ainslie. “RealFormer: Transformer Likes Residual Attention”. In: ACL/IJCNLP. Vol. ACL/IJCNLP 2021. 2021, pp. 929–943.
[45] X. He, K. Deng, X. Wang, Y. Li, Y. Zhang, and M. Wang. “LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation”. In: SIGIR. 2020, pp. 639–648.
[46] X. He, L. Liao, H. Zhang, L. Nie, X. Hu, and T. Chua. “Neural Collaborative Filtering”. In: WWW. 2017, pp. 173–182.
[47] S. Hochreiter and J. Schmidhuber. “Long short-term memory”. In: Neural computation 9.8 (1997), pp. 1735–1780.
[48] M. Höfler. “Causal inference based on counterfactuals”. In: BMC Medical Research Methodology 5.1 (2005), pp. 1–12.
[49] B. Hu, C. Shi, W. X. Zhao, and P. S. Yu. “Leveraging meta-path based context for top-n recommendation with a neural co-attention model”. In: SIGKDD. 2018, pp. 1531–1540.7
[50] J. Huang, W. X. Zhao, H. Dou, J. Wen, and E. Y. Chang. “Improving Sequential Recommendation with Knowledge-Enhanced Memory Networks”. In: SIGIR. ACM, 2018, pp. 505–514.
[51] X. Huang, Q. Fang, S. Qian, J. Sang, Y. Li, and C. Xu. “Explainable Interaction-driven User Modeling over Knowledge Graph for Sequential Recommendation”. In: MM. ACM, 2019, pp. 548–556.
[52] Z. Huang, Y. Zheng, R. Cheng, Y. Sun, N. Mamoulis, and X. Li. “Meta structure: Computing relevance in large heterogeneous information networks”. In: SIGKDD. 2016, pp. 1595–1604.
[53] P. J. Huber. “Robust Estimation of a Location Parameter”. In: Ann. Math. Stat. 35.1 (1964), pp. 73–101.
[54] A. Javaloy and I. Valera. “RotoGrad: Gradient Homogenization in Multitask Learning”. In: ICLR. 2022.
[55] H. Ji, J. Zhu, X. Wang, C. Shi, B. Wang, X. Tan, Y. Li, and S. He. “Who You Would Like to Share With? A Study of Share Recommendation in Social E-commerce”. In: AAAI. AAAI Press, 2021, pp. 232–239.
[56] Y. Ji, A. Sun, J. Zhang, and C. Li. “A Re-visit of the Popularity Baseline in Recommender Systems”. In: SIGIR. 2020, pp. 1749– 1752.
[57] Y. Jiang, Q. Li, H. Zhu, J. Yu, J. Li, Z. Xu, H. Dong, and B. Zheng. “Adaptive Domain Interest Network for Multi-domain Recommendation”. In: CIKM. 2022, pp. 3212–3221.
[58] T. Kamishima, S. Akaho, H. Asoh, and J. Sakuma. “Correcting Popularity Bias by Enhancing Recommendation Neutrality”. In: RecSys. Vol. 1247. 2014.y
[59] W.-C. Kang and J. McAuley. “Candidate Generation with Binary Codes for Large-Scale Top-N Recommendation”. In: CIKM. 2019, pp. 1523–1532.
[60] D. P. Kingma and J. Ba. “Adam: A Method for Stochastic Optimization”. In: ICLR. 2015.
[61] T. N. Kipf and M. Welling. “Semi-Supervised Classification with Graph Convolutional Networks”. In: ICLR. 2017.
[62] J. Kitazono, N. Grozavu, N. Rogovschi, T. Omori, and S. Ozawa. “t-Distributed Stochastic Neighbor Embedding with Inhomogeneous Degrees of Freedom”. In: ICONIP. Vol. 9949. 2016, pp. 119– 128.
[63] J. Klicpera, A. Bojchevski, and S. Günnemann. “Predict then Propagate: Graph Neural Networks meet Personalized PageRank”. In: ICLR. 2019.
[64] S. Ko and J. Lee. “User Preference Mining through Collaborative Filtering and Content Based Filtering in Recommender System”. In: EC-Web. Vol. 2455. 2002, pp. 244–253.
[65] Y. Koren, R. Bell, and C. Volinsky. “Matrix Factorization Techniques for Recommender Systems”. In: Computer 42.8 (2009), pp. 30– 37. DOI: 10.1109/MC.2009.263.
[66] D. Kowald, G. Mayr, M. Schedl, and E. Lex. “A Study on Accuracy, Miscalibration, and Popularity Bias in Recommendations”. In: CoRR abs/2303.00400 (2023).9
[67] P. V. S. Kumar, A. Thahsin, M. M, and G. G. “A Heterogeneous Information Network Model for Long Non-Coding RNA Function Prediction”. In: IEEE ACM Trans. Comput. Biol. Bioinform. 19.1 (2022), pp. 255–266.
[68] O. Lesota, A. B. Melchiorre, N. Rekabsaz, S. Brandl, D. Kowald, E. Lex, and M. Schedl. “Analyzing Item Popularity Bias of Music Recommender Systems: Are Different Genders Equally Affected?” In: RecSys. 2021, pp. 601–606.
[69] C.Li,Y.Xie,C.Yu,B.Hu,Z.Li,G.Shu,X.Qie,andD.Niu.“One for All, All for One: Learning and Transferring User Embeddings for Cross-Domain Recommendation”. In: WSDM. 2023, pp. 366– 374.
[70] H. Li, Y. Shao, J. Du, B. Cui, and L. Chen. “An I/O-Efficient Diskbased Graph System for Scalable Second-Order Random Walk of Large Graphs”. In: PVLDB (2022).
[71] H. Li, Y. Wang, Z. Lyu, and J. Shi. “Multi-Task Learning for Recommendation Over Heterogeneous Information Network”. In: TKDE 34.2 (2022), pp. 789–802.
[72] L. Li, K. G. Jamieson, G. DeSalvo, A. Rostamizadeh, and A. Talwalkar. “Hyperband: A Novel Bandit-Based Approach to Hyper-parameter Optimization”. In: JMLR 18 (2017), 185:1–185:52.
[73] P. Li, R. Li, Q. Da, A. Zeng, and L. Zhang. “Improving MultiScenario Learning to Rank in E-commerce by Exploiting Task Relationships in the Label Space”. In: CIKM. 2020, pp. 2605–2612.
[74] X. Li and P. Li. “Rejection Sampling for Weighted Jaccard Similarity Revisited”. In: AAAI. 2021, pp. 4197–4205.y
[75] B. Lin and Y. Zhang. “LibMTL: A Python Library for Multi-Task Learning”. In: arXiv preprint arXiv:2203.14338 (2022).
[76] Y. Lin, Z. Liu, M. Sun, Y. Liu, and X. Zhu. “Learning entity and relation embeddings for knowledge graph completion”. In: AAAI. 2015.
[77] B. Liu, X. Liu, X. Jin, P. Stone, and Q. Liu. “Conflict-Averse Gradient Descent for Multi-task learning”. In: NeurIPS. 2021, pp. 18878– 18890.
[78] F. Liu, Z. Cheng, L. Zhu, Z. Gao, and L. Nie. “Interest-aware Message-Passing GCN for Recommendation”. In: WWW. 2021, pp. 1296– 1305.
[79] J. Liu, W. Huang, T. Li, S. Ji, and J. Zhang. “Cross-Domain Knowledge Graph Chiasmal Embedding for Multi-Domain Item-Item Recommendation”. In: TKDE 35.5 (2023), pp. 4621–4633.
[80] J. Liu, X. Li, B. An, Z. Xia, and X. Wang. “Multi-Faceted Hierarchical Multi-Task Learning for Recommender Systems”. In: CIKM. 2022, pp. 3332–3341.
[81] K. Liu, F. Xue, X. He, D. Guo, and R. Hong. “Joint Multi-Grained Popularity-Aware Graph Convolution Collaborative Filtering for Recommendation”. In: TCSS 10.1 (2023), pp. 72–83.
[82] M. Liu, J. Li, G. Li, and P. Pan. “Cross Domain Recommendation via Bi-directional Transfer Graph Collaborative Filtering Networks”. In: CIKM. 2020, pp. 885–894.
[83] S. Liu, Y. Liang, and A. Gitter. “Loss-Balanced Task Weighting to Reduce Negative Transfer in Multi-Task Learning”. In: AAAI. 2019, pp. 9977–9978.
[84] W. Liu, X. Zheng, M. Hu, and C. Chen. “Collaborative Filtering with Attribution Alignment for Review-based Non-overlapped Cross-Domain Recommendation”. In: WWW. 2022, pp. 1181–1190.
[85] L. Luo, Y. Li, B. Gao, S. Tang, S. Wang, J. Li, T. Zhu, J. Liu, Z. Li, B. Zhao, Z. Zheng, and S. Pan. “MAMDR: A Model Agnostic Learning Method for Multi-Domain Recommendation”. In: CoRR abs/2202.12524 (2022).
[86] Q. Lv, M. Ding, Q. Liu, Y. Chen, W. Feng, S. He, C. Zhou, J. Jiang, Y. Dong, and J. Tang. “Are we really making much progress?: Re-visiting, benchmarking and refining heterogeneous graph neural networks”. In: KDD. 2021, pp. 1150–1160.
[87] J. Ma, Z. Zhao, X. Yi, J. Chen, L. Hong, and E. H. Chi. “Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts”. In: SIGKDD. 2018, pp. 1930–1939.
[88] N. Ma, M. Ispir, Y. Li, Y. Yang, Z. Chen, D. Z. Cheng, L. Nie, and K. Barman. “An Online Multi-task Learning Framework for Google Feed Ads Auction Models”. In: SIGKDD. 2022, pp. 3477–3485.
[89] X. Ma, L. Zhao, G. Huang, Z. Wang, Z. Hu, X. Zhu, and K. Gai. “Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate”. In: SIGIR. 2018, pp. 1137– 1140.
[90] Y. A. Malkov and D. A. Yashunin. “Efficient and Robust Approximate Nearest Neighbor Search Using Hierarchical Navigable Small World Graphs”. In: TPAMI 42.4 (2020), pp. 824–836. y
[91] C. Meng, R. Cheng, S. Maniu, P. Senellart, and W. Zhang. “Discovering Meta-Paths in Large Heterogeneous Information Networks”. In: WWW. ACM, 2015, pp. 754–764.
[92] I. Misra, A. Shrivastava, A. Gupta, and M. Hebert. “Cross-Stitch Networks for Multi-task Learning”. In: CVPR. 2016, pp. 3994– 4003.
[93] V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski, et al. “Human-level control through deep reinforcement learning”. In: Nature 518.7540 (2015), pp. 529–533.
[94] J. Ni. Amazon review data. "https://nijianmo.github.io/amazon/ index.html".
[95] W. Ning, R. Cheng, J. Shen, N. A. H. Haldar, B. Kao, X. Yan, N. Huo, W. K. Lam, T. Li, and B. Tang. “Automatic Meta-Path Discovery for Effective Graph-Based Recommendation”. In: CIKM. 2022, pp. 1563–1572.
[96] W. Ning, R. Cheng, X. Yan, B. Kao, N. Huo, N. A. H. Haldar, and B. Tang. “Debiasing Recommendation with Popular Popularity”. In: WWW. 2024.
[97] W. Ning, X. Yan, W. Liu, R. Cheng, R. Zhang, and B. Tang. “Multidomain Recommendation with Embedding Disentangling and Domain Alignment”. In: CIKM. 2023.
[98] X. Niu, B. Li, C. Li, J. Tan, R. Xiao, and H. Deng. “Heterogeneous Graph Augmented Multi-Scenario Sharing Recommendation with Tree-Guided Expert Networks”. In: WSDM. 2021, pp. 1038– 1046.3
[99] Y. Niu, K. Tang, H. Zhang, Z. Lu, X. Hua, and J. Wen. “Counter-factual VQA: A Cause-Effect Look at Language Bias”. In: CVPR. 2021, pp. 12700–12710.
[100] R. Otunba, R. A. Rufai, and J. Lin. “MPR: Multi-Objective Pair-wise Ranking”. In: RecSys. 2017, pp. 170–178.
[101] J. Pearl. Causality. Cambridge University Press, 2009.
[102] C. Qian, F. Feng, L. Wen, C. Ma, and P. Xie. “Counter-factual Inference for Text Classification Debiasing.” In: ACL/IJCNLP. 2021, pp. 5434–5445.
[103] S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-Thieme. “BPR: Bayesian Personalized Ranking from Implicit Feedback”. In: CoRR abs/1205.2618 (2012).
[104] Y. Rong, W. Huang, T. Xu, and J. Huang. “DropEdge: Towards Deep Graph Convolutional Networks on Node Classification”. In: ICLR. 2020.
[105] H. Sak, A. W. Senior, and F. Beaufays. “Long short-term memory recurrent neural network architectures for large scale acoustic modeling”. In: INTERSPEECH. 2014, pp. 338–342.
[106] I. Sato, Y. Nomura, S. Hanaoka, S. Miki, N. Hayashi, O. Abe, and Y. Masutani. “Managing Computer-Assisted Detection System Based on Transfer Learning with Negative Transfer Inhibition”. In: SIGKDD. 2018, pp. 695–704.
[107] T. Schnabel, A. Swaminathan, A. Singh, N. Chandak, and T. Joachims. “Recommendations as Treatments: Debiasing Learning and Evaluation”. In: ICML. Vol. 48. JMLR Workshop and Conference Proceedings. 2016, pp. 1670–1679.
[108] X. Sheng, L. Zhao, G. Zhou, X. Ding, B. Dai, Q. Luo, S. Yang, J. Lv, C. Zhang, H. Deng, and X. Zhu. “One Model to Serve All: Star Topology Adaptive Recommender for Multi-Domain CTR Prediction”. In: CIKM. 2021, pp. 4104–4113.
[109] B. Shi and T. Weninger. “Mining Interesting Meta-Paths from Complex Heterogeneous Information Networks”. In: ICDM. IEEE Computer Society, 2014, pp. 488–495.
[110] C. Shi, B. Hu, W. X. Zhao, and P. S. Yu. “Heterogeneous Information Network Embedding for Recommendation”. In: TKDE (2018).
[111] T. Standley, A. R. Zamir, D. Chen, L. J. Guibas, J. Malik, and S. Savarese. “Which Tasks Should Be Learned Together in Multitask Learning?” In: ICML. Vol. 119. PMLR, 2020, pp. 9120–9132.
[112] Y. Su, R. Zhang, S. M. Erfani, and J. Gan. “Neural Graph Matching based Collaborative Filtering”. In: SIGIR. 2021, pp. 849–858.
[113] Y. Su, R. Zhang, S. M. Erfani, and Z. Xu. “Detecting Beneficial Feature Interactions for Recommender Systems”. In: AAAI. 2021, pp. 4357–4365.
[114] Y. Su, Y. Zhao, S. M. Erfani, J. Gan, and R. Zhang. “Detecting Arbitrary Order Beneficial Feature Interactions for Recommender Systems”. In: KDD. 2022, pp. 1676–1686.
[115] T. Sun, Y. Shao, X. Li, P. Liu, H. Yan, X. Qiu, and X. Huang. “Learning Sparse Sharing Architectures for Multiple Tasks”. In: AAAI. 2020, pp. 8936–8943.
[116] W. Sun, S. Khenissi, O. Nasraoui, and P. Shafto. “Debiasing the Human-Recommender System Feedback Loop in Collaborative Filtering”. In: WWW. 2019, pp. 645–651
[117] Y. Sun, J. Han, X. Yan, P. S. Yu, and T. Wu. “PathSim: Meta PathBased Top-K Similarity Search in Heterogeneous Information Networks”. In: PVLDB 4.11 (2011), pp. 992–1003.
[118] Z. Sun, J. Yang, J. Zhang, A. Bozzon, L. Huang, and C. Xu. “Recurrent knowledge graph embedding for effective recommendation”. In: RecSys. ACM, 2018, pp. 297–305.
[119] R. S. Sutton and A. G. Barto. “Reinforcement Learning: An Introduction”. In: IEEE Trans. Neural Networks 9.5 (1998), pp. 1054–1054.
[120] H. Tang, J. Liu, M. Zhao, and X. Gong. “Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations”. In: RecSys. 2020, pp. 269–278.
[121] K. Tang, Y. Niu, J. Huang, J. Shi, and H. Zhang. “Unbiased Scene Graph Generation From Biased Training”. In: CVPR. 2020, pp. 3713–3722.
[122] S. Tang, Q. Li, D. Wang, C. Gao, W. Xiao, D. Zhao, Y. Jiang, Q. Ma, and A. Zhang. “Counterfactual Video Recommendation for Duration Debiasing”. In: SIGKDD. 2023, pp. 4894–4903.
[123] Tianchi. Ad Display/Click Data. "https://tianchi.aliyun.com/dataset/dataDetail?dataId=56".
[124] C. Tu, X. Zeng, H. Wang, Z. Zhang, Z. Liu, M. Sun, B. Zhang, and L. Lin. “A Unified Framework for Community Detection and Network Representation Learning”. In: TKDE 31.6 (2019), pp. 1051–1065. DOI: 10.1109/TKDE.2018.2852958.
[125] P. Velickovic, G. Cucurull, A. Casanova, A. Romero, P. Liò, and Y. Bengio. “Graph Attention Networks”. In: ICLR. 2018.
[126] G. Wan, B. Du, S. Pan, and G. Haffari. “Reinforcement Learning Based Meta-Path Discovery in Large-Scale Heterogeneous Information Networks”. In: AAAI. AAAI Press, 2020, pp. 6094–6101.
[127] D. Wang, P. Liu, Y. Zheng, X. Qiu, and X.-J. Huang. “Heterogeneous Graph Neural Networks for Extractive Document Summarization”. In: ACL. 2020, pp. 6209–6219.
[128] H. Wang, F. Zhang, X. Xie, and M. Guo. “DKN: Deep KnowledgeAware Network for News Recommendation”. In: WWW. 2018, pp. 1835–1844.
[129] J. Wang, Y. Chen, Z. Wang, and W. Zhao. “Popularity-Enhanced News Recommendation with Multi-View Interest Representation”. In: CIKM. 2021, pp. 1949–1958.
[130] K. Wang, Y. Zhu, H. Liu, T. Zang, C. Wang, and K. Liu. “Interand Intra-Domain Relation-Aware Heterogeneous Graph Convolutional Networks for Cross-Domain Recommendation”. In: DASFAA. Vol. 13246. 2022, pp. 53–68.
[131] L. Wang, M. Zhang, Z. Jia, Q. Li, C. Bao, K. Ma, J. Zhu, and Y. Zhong. “AFEC: Active Forgetting of Negative Transfer in Continual Learning”. In: NeurIPS. 2021, pp. 22379–22391.
[132] W. Wang, F. Feng, X. He, H. Zhang, and T. S. Chua. “Clicks can be Cheating: Counterfactual Recommendation for Mitigating Clickbait Issue”. In: SIGIR. 2021, pp. 1288–1297.
[133] W. Wang, X. Lin, F. Feng, X. He, M. Lin, and T. S. Chua. “Causal Representation Learning for Out-of-Distribution Recommendation”. In: WWW. 2022, pp. 3562–3571.
[134] X. Wang, X. He, Y. Cao, M. Liu, and T.-S. Chua. “KGAT: Knowledge Graph Attention Network for Recommendation”. In: SIGKDD.2019.
[135] X. Wang, X. He, M. Wang, F. Feng, and T. Chua. “Neural GraphCollaborative Filtering”. In: SIGIR. 2019, pp. 165–174.
[136] X. Wang, D. Wang, C. Xu, X. He, Y. Cao, and T. Chua. “Explainable Reasoning over Knowledge Graphs for Recommendation”.In: AAAI. AAAI Press, 2019, pp. 5329–5336.
[137] Y. Wang, H. Guo, B. Chen, W. Liu, Z. Liu, Q. Zhang, Z. He, H.Zheng, W. Yao, M. Zhang, Z. Dong, and R. Tang. “CausalInt: CausalInspired Intervention for Multi-Scenario Recommendation”. In:KDD. 2022, pp. 4090–4099.
[138] Z. Wang, J. Zhang, J. Feng, and Z. Chen. “Knowledge Graph Embedding by Translating on Hyperplanes”. In: AAAI. 2014, pp. 1112–1119.
[139] T. Wei, F. Feng, J. Chen, Z. Wu, J. Yi, and X. He. “Model-AgnosticCounterfactual Reasoning for Eliminating Popularity Bias in Recommender System”. In: SIGKDD. 2021, pp. 1791–1800.
[140] F. Wu, A. H. S. Jr., T. Zhang, C. Fifty, T. Yu, and K. Q. Weinberger.“Simplifying Graph Convolutional Networks”. In: ICML. Vol. 97.2019, pp. 6861–6871.
[141] H. C. Wu, R. W. P. Luk, K. Wong, and K. Kwok. “InterpretingTF-IDF term weights as making relevance decisions”. In: TIS 26.3(2008), 13:1–13:37.
[142] Y. Wu and X. Huang. “A Gumbel-based Rating Prediction Framework for Imbalanced Recommendation”. In: CIKM. 2022, pp. 2199–2209.
[143] W. Xi, L. Huang, C. Wang, Y. Zheng, and J. Lai. “BPAM: Recommendation Based on BP Neural Network with Attention Mechanism”. In: IJCAI. 2019, pp. 3905–3911.
[144] W. Xiao, J. Houye, S. Chuan, W. Bai, C. Peng, Y. P., and Y. Yanfang.“Heterogeneous Graph Attention Network”. In: WWW (2019).
[145] R. Xie, Q. Liu, L. Wang, S. Liu, B. Zhang, and L. Lin. “ContrastiveCross-domain Recommendation in Matching”. In: SIGKDD. 2022,pp. 4226–4236.
[146] H. Xiong and J. Yan. “BTWalk: Branching Tree Random Walk forMulti-Order Structured Network Embedding”. In: TKDE 34.8 (2022),pp. 3611–3628.
[147] W. Xu, S. Li, M. Ha, X. Guo, Q. Ma, X. Liu, L. Chen, and Z. Zhu.“Neural Node Matching for Multi-Target Cross Domain Recommendation”. In: CoRR abs/2302.05919 (2023).
[148] Z. Xu, P. Wei, S. Liu, W. Zhang, L. Wang, and B. Zheng. “Correlative Preference Transfer with Hierarchical Hypergraph Networkfor Multi-Domain Recommendation”. In: WWW. 2023, pp. 983–991.
[149] G. Xv, C. Lin, H. Li, J. Su, W. Ye, and Y. Chen. “NeutralizingPopularity Bias in Recommendation Models”. In: SIGIR. 2022,pp. 2623–2628.
[150] X. Yang, F. Feng, W. Ji, M. Wang, and T. Chua. “DeconfoundedVideo Moment Retrieval with Causal Intervention”. In: SIGIR.2021, pp. 1–10.
[151] R. Ying, R. He, K. Chen, P. Eksombatchai, W. L. Hamilton, and J.Leskovec. “Graph Convolutional Neural Networks for Web-ScaleRecommender Systems”. In: KDD. 2018, pp. 974–983.
[152] T. Yu, S. Kumar, A. Gupta, S. Levine, K. Hausman, and C. Finn.“Gradient Surgery for Multi-Task Learning”. In: NeurIPS. 2020.
[153] X. Yu, X. Ren, Y. Sun, B. Sturt, U. Khandelwal, Q. Gu, B. Norick,and J. Han. “Recommendation in heterogeneous information networks with implicit user feedback”. In: RecSys. 2013, pp. 347–350.
[154] D. Zha, K. Lai, Y. Cao, S. Huang, R. Wei, J. Guo, and X. Hu. “RLCard: A Toolkit for Reinforcement Learning in Card Games”. In:CoRR abs/1910.04376 (2019).
[155] F. Zhang, Q. Peng, Y. Wu, Z. Pan, R. Zeng, D. Lin, and Y. Qi.“Multi-Graph based Multi-Scenario Recommendation in Largescale Online Video Services”. In: WWW Companion. 2022, pp. 1167–1175.
[156] F. Zhang, N. J. Yuan, D. Lian, X. Xie, and W. Ma. “Collaborative Knowledge Base Embedding for Recommender Systems”. In:SIGKDD. ACM, 2016, pp. 353–362.
[157] Q. Zhang, X. Zhang, Y. Liu, H. Wang, M. Gao, J. Zhang, and R.Guo. “Debiasing Recommendation by Learning Identifiable Latent Confounders”. In: SIGKDD. 2023, pp. 3353–3363.
[158] R. Zhang, B. D. Trisedya, M. Li, Y. Jiang, and J. Qi. “A benchmarkand comprehensive survey on knowledge graph entity alignmentvia representation learning”. In: VLDB J. 31.5 (2022), pp. 1143–1168.
[159] Y. Zhang, C. Li, I. W. Tsang, H. Xu, L. Duan, H. Yin, W. Li, and J.Shao. “Diverse Preference Augmentation with Multiple Domainsfor Cold-start Recommendations”. In: ICDE. 2022, pp. 2942–2955.
[160] Y. Zhang, F. Feng, X. He, T. Wei, C. Song, G. Ling, and Y. Zhang.“Causal Intervention for Leveraging Popularity Bias in Recommendation”. In: SIGIR. 2021, pp. 11–20.
[161] Y. Zhang, W. Wang, P. Wu, F. Feng, and X. He. “Causal Recommendation: Progresses and Future Directions”. In: WWW. 2022.
[162] Y. Zhang, X. Li, Y. Yu, J. Tang, H. Deng, J. Lu, Y. Zhang, Q. Jiang, Y.Xian, L. Yu, and H. Liu. “Meta-Generator Enhanced Multi-DomainRecommendation”. In: WWW. 2023, pp. 485–489.
[163] Y. Zhang, Q. Ai, X. Chen, and P. Wang. “Learning over KnowledgeBase Embeddings for Recommendation”. In: CoRR abs/1803.06540(2018).
[164] B. Zhao, L. Hu, Z. You, L. Wang, and X. Su. “HINGRL: predicting drug-disease associations with graph representation learning on heterogeneous information networks”. In: Briefings Bioinform.23.1 (2022).
[165] C. Zhao, C. Li, and C. Fu. “Cross-Domain Recommendation viaPreference Propagation GraphNet”. In: CIKM. 2019, pp. 2165–2168.
[166] C. Zhao, H. Zhao, M. HE, J. Zhang, and J. Fan. “Cross-domain recommendation via user interest alignment”. In: WWW. 2023,pp. 887–896.
[167] M. Zhao, L. Wu, Y. Liang, L. Chen, J. Zhang, Q. Deng, K. Wang, X. Shen, T. Lv, and R. Wu. “Investigating Accuracy-Novelty Performance for Graph-based Collaborative Filtering”. In: SIGIR. 2022, pp. 50–59.
[168] X. Zhao, N. Yang, and P. S. Yu. “Multi-Sparse-Domain Collaborative Recommendation via Enhanced Comprehensive Aspect Preference Learning”. In: WSDM. 2022, pp. 1452–1460.
[169] Z. Zhao, J. Chen, S. Zhou, X. He, X. Cao, F. Zhang, and W. Wu. “Popularity Bias Is Not Always Evil: Disentangling Benign and Harmful Bias for Recommendation”. In: TKDE (2021).
[170] J. Zheng, F. Cai, Y. Ling, and H. Chen. “Heterogeneous Graph Neural Networks to Predict What Happen Next”. In: COLING. 2020.
[171] Y. Zheng, C. Gao, X. Li, X. He, Y. Li, and D. Jin. “Disentangling user interest and conformity for recommendation with causal embedding”. In: WWW. 2021, pp. 2980–2991.
[172] Z. Zhong, C. Li, and J. Pang. “Reinforcement Learning Enhanced Heterogeneous Graph Neural Network”. In: CoRR abs/2010.13735 (2020).
[173] F. Zhu, Y. Wang, C. Chen, J. Zhou, L. Li, and G. Liu. “CrossDomain Recommendation: Challenges, Progress, and Prospects”. In: IJCAI. 2021, pp. 4721–4728.
[174] X. Zhu, Y. Zhang, F. Feng, X. Yang, D. Wang, and X. He. “Mitigating Hidden Confounding Effects for Causal Recommendation”. In: CoRR abs/2205.07499 (2022).
[175] Y. Zhu, J. Yi, J. Xie, and Z. Chen. “Deep Causal Reasoning for Recommendations”. In: CoRR abs/2201.02088 (2022).
[176] Y. Zhu, Z. Tang, Y. Liu, F. Zhuang, R. Xie, X. Zhang, L. Lin, and Q. He. “Personalized Transfer of User Preferences for Cross-domain Recommendation”. In: WSDM. 2022, pp. 1507–1515.
[177] Z. Zhu, Y. He, X. Zhao, Y. Zhang, J. Wang, and J. Caverlee. “Popularity-Opportunity Bias in Collaborative Filtering”. In: WSDM. 2021, pp. 85–93