research-article

Asymmetrical Attention Networks Fused Autoencoder for Debiased Recommendation

Authors:

Meng YuanAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology, Volume 14, Issue 6

Article No.: 100, Pages 1 - 24

https://doi.org/10.1145/3596498

Published: 14 November 2023 Publication History

Abstract

Popularity bias is a massive challenge for autoencoder-based models, which decreases the level of personalization and hurts the fairness of recommendations. User reviews reflect their preferences and help mitigate bias or unfairness in the recommendation. However, most existing works typically incorporate user (item) reviews into a long document and then use the same module to process the document in parallel. Actually, the set of user reviews is completely different from the set of item reviews. User reviews are heterogeneous in that they reflect a variety of items purchased by users, while item reviews are only related to the item itself and are thus typically homogeneous. In this article, a novel asymmetric attention network fused with autoencoders is proposed, which jointly learns representations from the user and item reviews and implicit feedback to perform recommendations. Specifically, we design an asymmetric attentive module to capture rich representations from user and item reviews, respectively, which solves data sparsity and explainable problems. Furthermore, to further address popularity bias, we apply a noise-contrastive estimation objective to learn high-quality “de-popularity” embedding via the decoder structure. A series of extensive experiments are conducted on four benchmark datasets to show that leveraging user review information can eliminate popularity bias and improve performance compared to various state-of-the-art recommendation techniques.

References

[1]

Vito Walter Anelli, Alejandro Bellogín, Tommaso Di Noia, and Claudio Pomo. 2021. Reenvisioning the comparison between neural collaborative filtering and matrix factorization. In Proceedings of the International Conference on Recommender Systems (RecSys’21). 521–529.

Digital Library

[2]

Rodrigo Borges and Kostas Stefanidis. 2021. On mitigating popularity bias in recommendations via variational autoencoders. In Proceedings of the International Conference on Annual ACM Symposium on Applied Computing. 1383–1389.

Digital Library

[3]

Rose Catherine and William Cohen. 2017. Transnets: Learning to transform for recommendation. In Proceedings of the International Conference on Recommender Systems (RecSys’17). 288–296.

Digital Library

[4]

Chong Chen, Min Zhang, Yiqun Liu, and Shaoping Ma. 2018. Neural attentional rating regression with review-level explanations. In Proceedings of the International World Wide Web Conference (WWW’18). 1583–1592.

Digital Library

[5]

Jiawei Chen, Hande Dong, Xiang Wang, Fuli Feng, Meng Wang, and Xiangnan He. 2023. Bias and debias in recommender system: A survey and future directions. ACM Trans. Inf. Syst. 41, 3 (2023), 1–39.

Digital Library

[6]

Rui Chen, Qingyi Hua, Yan-Shuo Chang, Bo Wang, Lei Zhang, and Xiangjie Kong. 2018. A survey of collaborative filtering-based recommender systems: From traditional methods to hybrid methods based on social networks. IEEE Access 6 (2018), 64301–64320.

[7]

Zhihong Chen, Jiawei Wu, Chenliang Li, Jingxu Chen, Rong Xiao, and Binqiang Zhao. 2022. Co-training disentangled domain adaptation network for leveraging popularity bias in recommenders. In Proceedings of the International Conference on Research and Development in Information Retrieval (SIGIR’22). 60–69.

Digital Library

[8]

Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). 1724–1734.

[9]

Tao Dai, Wenjun Yan, Kaiqi Zhang, Chen Qiu, Xiangmo Zhao, and Shirui Pan. 2021. Gated relational stacked denoising autoencoder with localized author embedding for global citation recommendation. Expert Syst. Appl. 184 (2021), 115359.

Digital Library

[10]

Tao Dai, Li Zhu, Yaxiong Wang, and Kathleen M. Carley. 2019. Attentive stacked denoising autoencoder with bi-lstm for personalized context-aware citation recommendation. IEEE/ACM Trans. Audio Speech Lang. Process. 28 (2019), 553–568.

Digital Library

[11]

Aminu Da’u and Naomie Salim. 2020. Recommendation system based on deep learning methods: A systematic review and new directions. Artif. Intell. Rev. 53, 4 (2020), 2709–2748.

[12]

Rana Forsati, Mehrdad Mahdavi, Mehrnoush Shamsfard, and Mohamed Sarwat. 2014. Matrix factorization with explicit trust and distrust side information for improved social recommendation. ACM Trans. Inf. Syst. 32, 4 (2014), 1–38.

Digital Library

[13]

Alireza Gharahighehi, Celine Vens, and Konstantinos Pliakos. 2021. Fair multi-stakeholder news recommender system with hypergraph ranking. Inf. Process. Manage. 58, 5 (2021), 102663.

Digital Library

[14]

Guodong Guo and Na Zhang. 2019. A survey on deep learning based face recognition. Comput. Vis. Image Understand. 189 (2019), 102805.

Digital Library

[15]

Jiayu Han, Lei Zheng, Yuanbo Xu, Bangzuo Zhang, Fuzhen Zhuang, S. Yu Philip, and Wanli Zuo. 2019. Adaptive deep modeling of users and items using side information for recommendation. IEEE Trans. Neural Netw. Learn. Syst. 31, 3 (2019), 737–748.

[16]

M. D. Zakir Hossain, Ferdous Sohel, Mohd Fairuz Shiratuddin, and Hamid Laga. 2019. A comprehensive survey of deep learning for image captioning. ACM Comput. Surv. 51, 6 (2019), 1–36.

Digital Library

[17]

Chao Huang, Xian Wu, Xuchao Zhang, Chuxu Zhang, Jiashu Zhao, Dawei Yin, and Nitesh V Chawla. 2019. Online purchase prediction via multi-scale modeling of behavior dynamics. In Proceedings of the International Conference on Knowledge Discovery & Data Mining (SIGKDD’19). 2613–2622.

Digital Library

[18]

Yogesh Jhamb, Travis Ebesu, and Yi Fang. 2018. Attentive contextual denoising autoencoder for recommendation. In Proceedings of the International Conference on Theory of Information Retrieval (SIGIR’18). 27–34.

Digital Library

[19]

Donghyun Kim, Chanyoung Park, Jinoh Oh, Sungyoung Lee, and Hwanjo Yu. 2016. Convolutional matrix factorization for document context-aware recommendation. In Proceedings of the 10th ACM Conference on Recommender Systems. 233–240.

Digital Library

[20]

Haoxuan Li, Yan Lyu, Chunyuan Zheng, and Peng Wu. 2023. TDR-CL: Targeted doubly robust collaborative learning for debiased recommendations. In Proceedings of the International Conference on Learning Representations (ICLR’23). 1–15.

[21]

Haoxuan Li, Yanghao Xiao, Chunyuan Zheng, and Peng Wu. 2023. Balancing unobserved confounding with a few unbiased Ratings in Debiased Recommendations. In Proceedings of the ACM Web Conference. 2023, 1305–1313.

[22]

Jing Li, Aixin Sun, Jianglei Han, and Chenliang Li. 2020. A survey on deep learning for named entity recognition. IEEE Trans. Knowl. Data Eng. 34, 1 (2020), 50–70.

Digital Library

[23]

Xiaopeng Li and James She. 2017. Collaborative variational autoencoder for recommender systems. In Proceedings of the International ACM SIGIR Conference on Knowledge Discovery and Data Mining (SIGKDD’17). 305–314.

Digital Library

[24]

Guang Ling, Michael R. Lyu, and Irwin King. 2014. Ratings meet reviews, a combined approach to recommend. In Proceedings of the International Conference on Recommender systems (RecSys’14). 105–112.

Digital Library

[25]

Hongtao Liu, Yian Wang, Qiyao Peng, Fangzhao Wu, Lin Gan, Lin Pan, and Pengfei Jiao. 2020. Hybrid neural recommendation with joint deep representation learning of ratings and reviews. Neurocomputing 374 (2020), 77–85.

[26]

Weibo Liu, Zidong Wang, Xiaohui Liu, Nianyin Zeng, Yurong Liu, and Fuad E. Alsaadi. 2017. A survey of deep neural network architectures and their applications. Neurocomputing 234 (2017), 11–26.

[27]

Yat Hong Low, Wun-She Yap, and Yee Kai Tee. 2018. Convolutional neural network-based collaborative filtering for recommendation systems. In Proceedings of the International Conference on Robot Intelligence Technology and Applications (RiTA’18). Springer, 117–131.

[28]

Chen Ma, Peng Kang, Bin Wu, Qinglong Wang, and Xue Liu. 2019. Gated attentive-autoencoder for content-aware recommendation. In Proceedings of the International Conference on Web Search and Data Mining (WSDM’19). 519–527.

Digital Library

[29]

Hao Ma, Haixuan Yang, Michael R. Lyu, and Irwin King. 2008. Sorec: Social recommendation using probabilistic matrix factorization. In Proceedings of the International Conference on Information and Knowledge Management (CIKM’08). 931–940.

Digital Library

[30]

Julian McAuley and Jure Leskovec. 2013. Hidden factors and hidden topics: Understanding rating dimensions with review text. In Proceedings of the International Conference on Recommender Systems ((RecSys’13). 165–172.

Digital Library

[31]

Ruihui Mu. 2018. A survey of recommender systems based on deep learning. IEEE Access 6 (2018), 69009–69022.

[32]

Jianmo Ni, Larry Muhlstein, and Julian McAuley. 2019. Modeling heart rate and activity data for personalized fitness recommendation. In Proceedings of the The World Wide Web Conference (WWW’19). 1343–1353.

Digital Library

[33]

Daniel W. Otter, Julian R. Medina, and Jugal K. Kalita. 2020. A survey of the usages of deep learning for natural language processing. IEEE Trans. Neural Netw. Learn. Syst. 32, 2 (2020), 604–624.

[34]

Samira Pouyanfar, Saad Sadiq, Yilin Yan, Haiman Tian, Yudong Tao, Maria Presa Reyes, Mei-Ling Shyu, Shu-Ching Chen, and Sundaraja S. Iyengar. 2018. A survey on deep learning: Algorithms, techniques, and applications. ACM Comput. Surv. 51, 5 (2018), 1–36.

Digital Library

[35]

Zhaopeng Qiu, Xian Wu, Jingyue Gao, and Wei Fan. 2021. U-BERT: Pre-training user representations for improved recommendation. In Proceedings of the International Conference on Artificial Intelligence (AAAI’21). 1–8.

[36]

Jinfeng Rao, Hua He, and Jimmy Lin. 2016. Noise-contrastive estimation for answer selection with deep neural networks. In Proceedings of the International Conference on Information and Knowledge Management (CIKM’16). 1913–1916.

Digital Library

[37]

Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2012. BPR: Bayesian personalized ranking from implicit feedback. arXiv:1205.2618. Retrieved from https://arxiv.org/abs/1205.2618.

[38]

Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl. 2002. Incremental singular value decomposition algorithms for highly scalable recommender systems. In Proceedings of the International Conference on Computer and Information Science, Vol. 1. Citeseer, 27–8.

[39]

Suvash Sedhain, Aditya Krishna Menon, Scott Sanner, and Lexing Xie. 2015. Autorec: Autoencoders meet collaborative filtering. In Proceedings of the International Conference on World Wide Web (WWW’15). 111–112.

Digital Library

[40]

Ilya Shenbin, Anton Alekseev, Elena Tutubalina, Valentin Malykh, and Sergey I. Nikolenko. 2020. Recvae: A new variational autoencoder for top-n recommendations with implicit feedback. In Proceedings of the International Conference on Web Search and Data Mining (WSDM’20). 528–536.

Digital Library

[41]

Xiaoyuan Su and Taghi M. Khoshgoftaar. 2009. A survey of collaborative filtering techniques. Adv. Artif. Intell. (2009).

Digital Library

[42]

Poonam B. Thorat, R. M. Goudar, and Sunita Barve. 2015. Survey on collaborative filtering, content-based filtering and hybrid recommendation system. Int. J. Comput. Appl. 110, 4 (2015), 31–36.

[43]

Hao Wang, Xingjian Shi, and Dit-Yan Yeung. 2016. Collaborative recurrent autoencoder: Recommend while learning to fill in the blanks. Adv. Neural Inf. Process. Syst. 29 (2016).

[44]

Tianxin Wei, Fuli Feng, Jiawei Chen, Ziwei Wu, Jinfeng Yi, and Xiangnan He. 2021. Model-agnostic counterfactual reasoning for eliminating popularity bias in recommender system. In Proceedings of the International Conference on Knowledge Discovery and Data Mining (KDD’21). 1791–1800.

Digital Library

[45]

Chuhan Wu, Fangzhao Wu, Junxin Liu, and Yongfeng Huang. 2019. Hierarchical user and item representation with three-tier attention for recommendation. In Proceedings of the International Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 1818–1826.

[46]

Ga Wu, Maksims Volkovs, Chee Loong Soon, Scott Sanner, and Himanshu Rai. 2019. Noise contrastive estimation for one-class collaborative filtering. In Proceedings of the International Conference on Research and Development in Information Retrieval (SIGIR’19). 135–144.

Digital Library

[47]

Yao Wu, Christopher DuBois, Alice X. Zheng, and Martin Ester. 2016. Collaborative denoising auto-encoders for top-n recommender systems. In Proceedings of the International Conference on Web Search and Data Mining (WSDM’16). 153–162.

Digital Library

[48]

Zhe Xie, Chengxuan Liu, Yichi Zhang, Hongtao Lu, Dong Wang, and Yue Ding. 2021. Adversarial and contrastive variational autoencoder for sequential recommendation. In Proceedings of the International the Web Conference (WWW’21). 449–459.

Digital Library

[49]

Wenjie Yan, Dong Wang, Mengjing Cao, and Jing Liu. 2019. Deep auto encoder model with convolutional text networks for video recommendation. IEEE Access 7 (2019), 40333–40346.

[50]

Tom Young, Devamanyu Hazarika, Soujanya Poria, and Erik Cambria. 2018. Recent trends in deep learning based natural language processing. IEEE Comput. Intell. Mag. 13, 3 (2018), 55–75.

[51]

Syed Sahil Abbas Zaidi, Mohammad Samar Ansari, Asra Aslam, Nadia Kanwal, Mamoona Asghar, and Brian Lee. 2022. A survey of modern deep learning based object detection models. Digit. Sign. Process. (2022), 103514.

[52]

An Zhang, Wenchang Ma, Xiang Wang, and Tat-Seng Chua. 2022. Incorporating bias-aware margins into contrastive loss for collaborative filtering. In Proceedings of the International Conference on Neural Information Processing Systems (NIPS’22). 1–13.

[53]

Chunhong Zhang, Tiantian Li, Zhibin Ren, Zheng Hu, and Yang Ji. 2019. Taxonomy-aware collaborative denoising autoencoder for personalized recommendation. Appl. Intell. 49, 6 (2019), 2101–2118.

Digital Library

[54]

Guijuan Zhang, Yang Liu, and Xiaoning Jin. 2020. A survey of autoencoder-based recommender systems. Front. Comput. Sci. 14, 2 (2020), 430–450.

Digital Library

[55]

Hangbin Zhang, Raymond K. Wong, and Victor W. Chu. 2021. Hybrid variational autoencoder for recommender systems. ACM Trans. Knowl. Discov. Data 16, 2 (2021), 1–37.

[56]

Shuai Zhang, Lina Yao, Aixin Sun, and Yi Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM Comput. Surv. 52, 1 (2019), 1–38.

Digital Library

[57]

Shuai Zhang, Lina Yao, and Xiwei Xu. 2017. Autosvd++ an efficient hybrid collaborative filtering model via contractive auto-encoders. In Proceedings of the International Conference on Research and Development in Information Retrieval (SIGIR’17). 957–960.

[58]

Yang Zhang, Fuli Feng, Xiangnan He, Tianxin Wei, Chonggang Song, Guohui Ling, and Yongdong Zhang. 2021. Causal intervention for leveraging popularity bias in recommendation. In Proceedings of the International Conference on Research and Development in Information Retrieval (SIGIR’21). 11–20.

Digital Library

[59]

Yihao Zhang, Zhi Liu, and Chunyan Sang. 2021. Unifying paragraph embeddings and neural collaborative filtering for hybrid recommendation. Appl. Soft Comput. 106 (2021), 107345.

Digital Library

[60]

Lei Zheng, Vahid Noroozi, and Philip S. Yu. 2017. Joint deep modeling of users and items using reviews for recommendation. In Proceedings of the International Conference on Web Search and Data Mining (WSDM’17). 425–434.

Digital Library

[61]

Shuai Zheng, Chris Ding, and Feiping Nie. 2018. Regularized singular value decomposition and application to recommender system. arXiv:1804.05090. Retrieved from https://arxiv.org/abs/1804.05090.

[62]

Jin Peng Zhou, Zhaoyue Cheng, Felipe Pérez, and Maksims Volkovs. 2020. TAFA: Two-headed attention fused autoencoder for context-aware recommendations. In Proceedings of the International Conference on Recommender Systems (RecSys’20). 338–347.

Digital Library

[63]

Jin Peng Zhou, Ga Wu, Zheda Mai, and Scott Sanner. 2020. Noise contrastive estimation for autoencoding-based one-class collaborative filtering. arXiv:2008.01246. Retrieved from https://arxiv.org/abs/2008.01246.

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Index Terms

Asymmetrical Attention Networks Fused Autoencoder for Debiased Recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
      1. Top-k retrieval in databases
  2. Information systems applications
    1. Data mining
      1. Collaborative filtering

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cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 14, Issue 6

December 2023

493 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/3632517

Editor:
Huan Liu
Arizona State University, USA

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Publication History

Published: 14 November 2023

Online AM: 17 May 2023

Accepted: 30 April 2023

Revised: 28 March 2023

Received: 27 June 2022

Published in TIST Volume 14, Issue 6

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Science and Technology Research Program of Chongqing Municipal Education Commission
Humanities and Social Sciences Research Program of Chongqing Municipal Education Commission
National Natural Science Foundation of China

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Cited By

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https://dl.acm.org/doi/10.1145/3673237
Yi JChen Z(2024)Deconfounded Cross-modal Matching for Content-based Micro-video Background Music RecommendationACM Transactions on Intelligent Systems and Technology10.1145/365004215:3(1-25)Online publication date: 15-Apr-2024
https://dl.acm.org/doi/10.1145/3650042
Madhusudhanan SJose ASahoo JMalekian R(2024)PRIMϵ: Novel Privacy-Preservation Model With Pattern Mining and Genetic AlgorithmIEEE Transactions on Information Forensics and Security10.1109/TIFS.2023.332476919(571-585)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TIFS.2023.3324769
Li YChen HXu SGe YTan JLiu SZhang Y(2023)Fairness in Recommendation: Foundations, Methods, and ApplicationsACM Transactions on Intelligent Systems and Technology10.1145/361030214:5(1-48)Online publication date: 9-Oct-2023
https://dl.acm.org/doi/10.1145/3610302
Zhao JHuang CWang WXie RDong RMatwin S(2023)Local differentially private federated learning with homomorphic encryptionThe Journal of Supercomputing10.1007/s11227-023-05378-x79:17(19365-19395)Online publication date: 27-May-2023
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