research-article

Multi-auxiliary Augmented Collaborative Variational Auto-encoder for Tag Recommendation

Authors:

Zhenzhong ChenAuthors Info & Claims

ACM Transactions on Information Systems, Volume 41, Issue 4

Article No.: 106, Pages 1 - 25

https://doi.org/10.1145/3578932

Published: 08 April 2023 Publication History

Abstract

Recommending appropriate tags to items can facilitate content organization, retrieval, consumption, and other applications, where hybrid tag recommender systems have been utilized to integrate collaborative information and content information for better recommendations. In this article, we propose a multi-auxiliary augmented collaborative variational auto-encoder (MA-CVAE) for tag recommendation, which couples item collaborative information and item multi-auxiliary information, i.e., content and social graph, by defining a generative process. Specifically, the model learns deep latent embeddings from different item auxiliary information using variational auto-encoders (VAE), which could form a generative distribution over each auxiliary information by introducing a latent variable parameterized by deep neural network. Moreover, to recommend tags for new items, item multi-auxiliary latent embeddings are utilized as a surrogate through the item decoder for predicting recommendation probabilities of each tag, where reconstruction losses are added in the training phase to constrain the generation for feedback predictions via different auxiliary embeddings. In addition, an inductive variational graph auto-encoder is designed to infer latent embeddings of new items in the test phase, such that item social information could be exploited for new items. Extensive experiments on MovieLens and citeulike datasets demonstrate the effectiveness of our method.

References

[1]

Bahare Askari, Jaroslaw Szlichta, and Amirali Salehi-Abari. 2021. Variational autoencoders for top-k recommendation with implicit feedback. In International ACM SIGIR Conference on Research and Development in Information Retrieval. 2061–2065.

Digital Library

[2]

Fabiano M. Belém, Jussara M. Almeida, and Marcos A. Gonçalves. 2017. A survey on tag recommendation methods. J. Assoc. Inf. Sci. Technol. 68, 4 (2017), 830–844.

Digital Library

[3]

David M. Blei, Alp Kucukelbir, and Jon D. McAuliffe. 2017. Variational inference: A review for statisticians. J. Amer. Statist. Assoc. 112, 518 (2017), 859–877.

[4]

Jian Chen, Lan Du, and Leiyao Liao. 2022. Discriminative mixture variational autoencoder for semisupervised classification. IEEE Trans. Cybern. 52, 5 (2022), 3032–3046.

[5]

Kaixuan Chen, Lina Yao, Dalin Zhang, Xianzhi Wang, Xiaojun Chang, and Feiping Nie. 2020. A semisupervised recurrent convolutional attention model for human activity recognition. IEEE Trans. Neural Netw. Learn. Syst. 31, 5 (2020), 1747–1756.

[6]

Xuewen Chen, Yonghong Yu, Fengyixin Jiang, Li Zhang, Rong Gao, and Haiyan Gao. 2020. Graph neural networks boosted personalized tag recommendation algorithm. In International Joint Conference on Neural Networks.1–8.

[7]

Yifan Chen and Maarten de Rijke. 2018. A collective variational autoencoder for top-n recommendation with side information. In Workshop on Deep Learning Recommender Systems.3–9.

[8]

Yifan Chen, Yang Wang, Xiang Zhao, Hongzhi Yin, Ilya Markov, and Maarten De Rijke. 2020. Local variational feature-based similarity models for recommending top-N new items. ACM Trans. Inf. Syst. 38, 2 (2020), 1046–8188.

Digital Library

[9]

Xiaomin Fang, Rong Pan, Guoxiang Cao, Xiuqiang He, and Wenyuan Dai. 2015. Personalized tag recommendation through nonlinear tensor factorization using gaussian kernel. In AAAI Conference on Artificial Intelligence.439–445.

[10]

Flavio Figueiredo, Henrique Pinto, Fabiano Belém, Jussara Almeida, Marcos Gonçalves, David Fernandes, and Edleno Moura. 2013. Assessing the quality of textual features in social media. Int. J. Inf. Process. Manag. 49 (2013), 222–247.

Digital Library

[11]

Ido Guy, Naama Zwerdling, Inbal Ronen, David Carmel, and Erel Uziel. 2010. Social media recommendation based on people and tags. In International ACM SIGIR Conference on Research and Development in Information Retrieval. 194–201.

Digital Library

[12]

William L. Hamilton, Rex Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. In International Conference on Advances in Neural Information Processing Systems.1025–1035.

[13]

Peng Hao, Guangquan Zhang, Luis Martinez, and Jie Lu. 2019. Regularizing knowledge transfer in recommendation With tag-inferred correlation. IEEE Trans. Cybern. 49, 1 (2019), 83–96.

[14]

Hebatallah A. Mohamed Hassan, Giuseppe Sansonetti, Fabio Gasparetti, and Alessandro Micarelli. 2018. Semantic-based tag recommendation in scientific bookmarking systems. In ACM Conference on Recommender Systems.465–469.

Digital Library

[15]

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Identity mappings in deep residual networks. In European Conference on Computer Vision.630–645.

[16]

I. Higgins, L. Matthey, A. Pal, Christopher P. Burgess, Xavier Glorot, M. Botvinick, S. Mohamed, and Alexander Lerchner. 2017. Beta-VAE: Learning basic visual concepts with a constrained variational framework. In International Conference on Learning Representations.

[17]

Navid Khezrian, Jafar Habibi, and Issa Annamoradnejad. 2020. Tag recommendation for online Q&A communities based on BERT pre-training technique. arXiv preprint arXiv:2010.04971 (2020).

[18]

Diederik P. Kingma and Max Welling. 2014. Auto-encoding variational Bayes. In International Conference on Learning Representations.

[19]

Thomas N. Kipf and Max Welling. 2016. Variational graph auto-encoders. In International Conference on Advances in Neural Information Processing Systems Workshop.

[20]

Yehuda Koren, Robert Bell, and Chris Volinsky. 2009. Matrix factorization techniques for recommender systems. Computer 42, 8 (2009), 30–37.

Digital Library

[21]

Wonsung Lee, Kyungwoo Song, and Il-Chul Moon. 2017. Augmented variational autoencoders for collaborative filtering with auxiliary information. In ACM Conference on Information Knowledge Managment.1139–1148.

Digital Library

[22]

Jia Li, Hua Xu, Xingwei He, Junhui Deng, and Xiaomin Sun. 2016. Tweet modeling with LSTM recurrent neural networks for hashtag recommendation. In International Joint Conference on Neural Networks.1570–1577.

[23]

Xin Li, Lei Guo, and Yihong Eric Zhao. 2008. Tag-based social interest discovery. In International Conference on World Wide Web. 675–684.

Digital Library

[24]

Xiaopeng Li and James She. 2017. Collaborative variational autoencoder for recommender systems. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 305–314.

Digital Library

[25]

Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman, and Tony Jebara. 2018. Variational autoencoders for collaborative filtering. In International Conference on World Wide Web. 689–698.

Digital Library

[26]

Minnan Luo, Xiaojun Chang, Liqiang Nie, Yi Yang, Alexander G. Hauptmann, and Qinghua Zheng. 2018. An adaptive semisupervised feature analysis for video semantic recognition. IEEE Trans. Cybern. 48, 2 (2018), 648–660.

[27]

Chao Ma, Wenbo Gong, José Miguel Hernández-Lobato, Noam Koenigstein, Sebastian Nowozin, and Cheng Zhang. 2018. Partial VAE for hybrid recommender system. In NIPS Workshop on Bayesian Deep Learning.

[28]

Suman Kalyan Maity, Abhishek Panigrahi, Sayan Ghosh, Arundhati Banerjee, Pawan Goyal, and Animesh Mukherjee. 2019. DeepTagRec: A content-cum-user based tag recommendation framework for stack overflow. In European Conference on Information Retrieval. 125–131.

Digital Library

[29]

Liqiang Nie, Yongqi Li, Fuli Feng, Xuemeng Song, Meng Wang, and Yinglong Wang. 2020. Large-scale question tagging via joint question-topic embedding learning. ACM Trans. Inf. Syst. 38, 2 (2020), 1046–8188.

Digital Library

[30]

Liqiang Nie, Yi-Liang Zhao, Xiangyu Wang, Jialie Shen, and Tat-Seng Chua. 2014. Learning to recommend descriptive tags for questions in social forums. ACM Trans. Inf. Syst. 32, 1 (2014), 1046–8188.

Digital Library

[31]

Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. 2014. DeepWalk: Online learning of social representations. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 701–710.

Digital Library

[32]

Daniel Ramage, David Hall, Ramesh Nallapati, and Christopher D. Manning. 2009. Labeled LDA: A supervised topic model for credit attribution in multi-labeled corpora. In Conference on Empirical Methods in Natural Language Processing.248–256.

[33]

Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian personalized ranking from implicit feedback. In Conference on Uncertainty in Artificial Intellience.452–461.

[34]

Steffen Rendle and Lars Schmidt-Thieme. 2009. Factor models for tag recommendation in bibsonomy. In International Conference on ECML PKDD Discovery Challenge. 235–242.

[35]

Steffen Rendle and Lars Schmidt-Thieme. 2010. Pairwise interaction tensor factorization for personalized tag recommendation. In ACM International Conference on Web Search and Data Mining. 81–90.

Digital Library

[36]

Kihyuk Sohn, Honglak Lee, and Xinchen Yan. 2015. Learning structured output representation using deep conditional generative models. In International Conference on Advances in Neural Information Processing Systems.3483–3491.

[37]

Yang Song, Lu Zhang, and C. Lee Giles. 2011. Automatic tag recommendation algorithms for social recommender systems. ACM Trans. Web 5, 1 (2011), 1–31.

Digital Library

[38]

Jianshan Sun, Mingyue Zhu, Yuanchun Jiang, Yezheng Liu, and Le Wu. 2021. Hierarchical attention model for personalized tag recommendation. J. Assoc. Inf. Sci. Technol. 72, 2 (2021), 173–189.

Digital Library

[39]

Masahiro Suzuki, Kotaro Nakayama, and Yutaka Matsuo. 2017. Joint multimodal learning with deep generative models. In International Conference on Learning Representations.

[40]

Panagiotis Symeonidis, Alexandros Nanopoulos, and Yannis Manolopoulos. 2008. Tag recommendations based on tensor dimensionality reduction. In ACM Conference on Recommender Systems.43–50.

Digital Library

[41]

Shijie Tang, Yuan Yao, Suwei Zhang, Feng Xu, Tianxiao Gu, Hanghang Tong, Xiaohui Yan, and Jian Lu. 2019. An integral tag recommendation model for textual content. In AAAI Conference on Artificial Intelligence.5109–5116.

Digital Library

[42]

Jesse Vig, Shilad Sen, and John Riedl. 2012. The tag genome: Encoding community knowledge to support novel interaction. ACM Trans. Interact. Intell. Syst. 2, 3 (2012), 1–44.

Digital Library

[43]

Chong Wang and David M. Blei. 2011. Collaborative topic modeling for recommending scientific articles. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 448–456.

Digital Library

[44]

Chaojie Wang, Bo Chen, Sucheng Xiao, Zhengjue Wang, Hao Zhang, Penghui Wang, Ning Han, and Mingyuan Zhou. 2021. Multimodal Weibull variational autoencoder for jointly modeling image-text data. IEEE Trans. Cybern. 52, 10 (2022), 11156–11171.

[45]

Chao Wang, Hengshu Zhu, Peng Wang, Chen Zhu, Xi Zhang, Enhong Chen, and Hui Xiong. 2021. Personalized and explainable employee training course recommendations: A Bayesian variational approach. ACM Trans. Inf. Syst. 40, 4 (2021), 1046–8188.

[46]

Hao Wang, Binyi Chen, and Wu-Jun Li. 2013. Collaborative topic regression with social regularization for tag recommendation. In International Joint Conference on Artificial Intelligence.2719–2725.

[47]

Hao Wang, Naiyan Wang, and Dit-Yan Yeung. 2015. Collaborative deep learning for recommender systems. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1235–1244.

Digital Library

[48]

Yong Wu, Shengqu Xi, Yuan Yao, Feng Xu, Hanghang Tong, and Jian Lu. 2018. Guiding supervised topic modeling for content based tag recommendation. Neurocomputing 314 (2018), 479–489.

[49]

Ping Yang, Yan Song, and Yang Ji. 2015. Tag-based user interest discovery though keywords extraction in social network. In International Conerence on Big Data Computing and Communications.363–372.

[50]

Jiahao Yuan, Yuanyuan Jin, Wenyan Liu, and Xiaoling Wang. 2019. Attention-based neural tag recommendation. In International Conference on Database Systems and Advanced Applications.350–365.

Digital Library

[51]

Dalin Zhang, Lina Yao, Kaixuan Chen, Sen Wang, Xiaojun Chang, and Yunhao Liu. 2020. Making sense of spatio-temporal preserving representations for EEG-based human intention recognition. IEEE Trans. Cybern. 50, 7 (2020), 3033–3044.

[52]

Haijun Zhang, Yanfang Sun, Mingbo Zhao, Tommy W. S. Chow, and Q. M. Jonathan Wu. 2020. Bridging user interest to item content for recommender systems: An optimization model. IEEE Trans. Cybern. 50, 10 (2020), 4268–4280.

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Lin LLi HLiu XPang SYuan MLiu X(2024)Graph Agent Transformer Network With Contrast Learning for Cross-Domain Recommendation of E-CommerceJournal of Cases on Information Technology10.4018/JCIT.35524126:1(1-16)Online publication date: 17-Jul-2024
https://dl.acm.org/doi/10.4018/JCIT.355241
Yu CFeng X(2024)Transfer contrast learning based on model-level data enhancement for cross-domain recommendationIntelligent Decision Technologies10.3233/IDT-24035218:2(717-729)Online publication date: 7-Jun-2024
https://doi.org/10.3233/IDT-240352
Jiang YChen GZhang WWang JJiang YZhang QLin JJiang PBian K(2024)Prompt Tuning for Item Cold-start RecommendationProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688126(411-421)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688126
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Index Terms

Multi-auxiliary Augmented Collaborative Variational Auto-encoder for Tag Recommendation
1. Information systems
  1. Information systems applications
    1. Multimedia information systems

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 41, Issue 4

October 2023

958 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/3587261

Editor:
Min Zhang
Tsinghua University, China

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Association for Computing Machinery

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Publication History

Published: 08 April 2023

Online AM: 31 January 2023

Accepted: 20 December 2022

Revised: 05 December 2022

Received: 28 April 2022

Published in TOIS Volume 41, Issue 4

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National Natural Science Foundation of China
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Cited By

Lin LLi HLiu XPang SYuan MLiu X(2024)Graph Agent Transformer Network With Contrast Learning for Cross-Domain Recommendation of E-CommerceJournal of Cases on Information Technology10.4018/JCIT.35524126:1(1-16)Online publication date: 17-Jul-2024
https://dl.acm.org/doi/10.4018/JCIT.355241
Yu CFeng X(2024)Transfer contrast learning based on model-level data enhancement for cross-domain recommendationIntelligent Decision Technologies10.3233/IDT-24035218:2(717-729)Online publication date: 7-Jun-2024
https://doi.org/10.3233/IDT-240352
Jiang YChen GZhang WWang JJiang YZhang QLin JJiang PBian K(2024)Prompt Tuning for Item Cold-start RecommendationProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688126(411-421)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688126
Ayemowa MIbrahim RAdamu Bena Y(2024)A Systematic Review of the Impact of Auxiliary Information on Recommender SystemsIEEE Access10.1109/ACCESS.2024.346275012(139524-139539)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3462750
Wang JChen GXin KFei Z(2024)Metric learning with adversarial hard negative samples for tag recommendationThe Journal of Supercomputing10.1007/s11227-024-06274-880:14(21475-21507)Online publication date: 11-Jun-2024
https://dl.acm.org/doi/10.1007/s11227-024-06274-8
Lai XJie Q(2023)Deep Semantic-Level Cross-Domain Recommendation Model Based on DSV-CDRMInternational Journal of Information Technology and Web Engineering10.4018/IJITWE.33363918:1(1-20)Online publication date: 15-Nov-2023
https://dl.acm.org/doi/10.4018/IJITWE.333639

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