Xiaolei Wang
ABSTRACT
Conversational recommender systems~(CRSs) aim to provide recommendation services via natural language conversations. Although a number of approaches have been proposed for developing capable CRSs, they typically rely on sufficient training data for training. Since it is difficult to annotate recommendation-oriented dialogue datasets, existing CRS approaches often suffer from the issue of insufficient training due to the scarcity of training data.
To address this issue, in this paper, we propose a CounterFactual data simulation approach for CRS, named CFCRS, to alleviate the issue of data scarcity in CRSs. Our approach is developed based on the framework of counterfactual data augmentation, which gradually incorporates the rewriting to the user preference from a real dialogue without interfering with the entire conversation flow. To develop our approach, we characterize user preference and organize the conversation flow by the entities involved in the dialogue, and design a multi-stage recommendation dialogue simulator based on a conversation flow language model. Under the guidance of the learned user preference and dialogue schema, the flow language model can produce reasonable, coherent conversation flows, which can be further realized into complete dialogues. Based on the simulator, we perform the intervention at the representations of the interacted entities of target users, and design an adversarial training method with a curriculum schedule that can gradually optimize the data augmentation strategy. Extensive experiments show that our approach can consistently boost the performance of several competitive CRSs, and outperform other data augmentation methods, especially when the training data is limited. Our code is publicly available at https://github.com/RUCAIBox/CFCRS.
Supplemental Material
- Hongshen Chen, Xiaorui Liu, Dawei Yin, and Jiliang Tang. 2017. A survey on dialogue systems: Recent advances and new frontiers. Acm Sigkdd Explorations Newsletter, Vol. 19, 2 (2017), 25--35.Google Scholar
Digital Library
- Qibin Chen, Junyang Lin, Yichang Zhang, Ming Ding, Yukuo Cen, Hongxia Yang, and Jie Tang. 2019. Towards Knowledge-Based Recommender Dialog System. In EMNLP. 1803--1813.Google Scholar
- Konstantina Christakopoulou, Filip Radlinski, and Katja Hofmann. 2016. Towards conversational recommender systems. In KDD. 815--824.Google Scholar
- Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In NAACL. 4171--4186.Google Scholar
- Steven Y Feng, Varun Gangal, Jason Wei, Sarath Chandar, Soroush Vosoughi, Teruko Mitamura, and Eduard Hovy. 2021. A survey of data augmentation approaches for NLP. arXiv preprint arXiv:2105.03075 (2021).Google Scholar
- Chongming Gao, Wenqiang Lei, Xiangnan He, Maarten de Rijke, and Tat-Seng Chua. 2021. Advances and challenges in conversational recommender systems: A survey. AI Open, Vol. 2 (2021), 100--126.Google ScholarCross Ref
- Jianfeng Gao, Michel Galley, and Lihong Li. 2018. Neural approaches to conversational AI. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. 1371--1374.Google Scholar
- Yash Goyal, Ziyan Wu, Jan Ernst, Dhruv Batra, Devi Parikh, and Stefan Lee. 2019. Counterfactual visual explanations. In International Conference on Machine Learning. PMLR, 2376--2384.Google Scholar
- Jiawei Han, Hong Cheng, Dong Xin, and Xifeng Yan. 2007. Frequent pattern mining: current status and future directions. Data mining and knowledge discovery, Vol. 15, 1 (2007), 55--86.Google Scholar
- Shirley Anugrah Hayati, Dongyeop Kang, Qingxiaoyang Zhu, Weiyan Shi, and Zhou Yu. 2020. INSPIRED: Toward Sociable Recommendation Dialog Systems. In EMNLP. 8142--8152.Google Scholar
- Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural collaborative filtering. In Proceedings of the 26th international conference on world wide web. 173--182.Google ScholarDigital Library
- Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2016. Session-based Recommendations with Recurrent Neural Networks. In 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, May 2-4, 2016, Conference Track Proceedings, Yoshua Bengio and Yann LeCun (Eds.). http://arxiv.org/abs/1511.06939Google Scholar
- Yutai Hou, Yijia Liu, Wanxiang Che, and Ting Liu. 2018. Sequence-to-Sequence Data Augmentation for Dialogue Language Understanding. In Proceedings of the 27th International Conference on Computational Linguistics. 1234--1245.Google Scholar
- Dietmar Jannach, Ahtsham Manzoor, Wanling Cai, and Li Chen. 2021. A survey on conversational recommender systems. ACM Computing Surveys (CSUR), Vol. 54, 5 (2021), 1--36.Google ScholarDigital Library
- Wang-Cheng Kang and Julian McAuley. 2018. Self-attentive sequential recommendation. In 2018 IEEE international conference on data mining (ICDM). IEEE, 197--206.Google ScholarCross Ref
- Ashutosh Kumar, Satwik Bhattamishra, Manik Bhandari, and Partha Talukdar. 2019. Submodular optimization-based diverse paraphrasing and its effectiveness in data augmentation. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 3609--3619.Google ScholarCross Ref
- Wenqiang Lei, Xiangnan He, Yisong Miao, Qingyun Wu, Richang Hong, Min-Yen Kan, and Tat-Seng Chua. 2020. Estimation-action-reflection: Towards deep interaction between conversational and recommender systems. In WSDM.Google Scholar
- Raymond Li, Samira Ebrahimi Kahou, Hannes Schulz, Vincent Michalski, Laurent Charlin, and Chris Pal. 2018. Towards deep conversational recommendations. NeurIPS, Vol. 31 (2018).Google Scholar
- Shuokai Li, Ruobing Xie, Yongchun Zhu, Xiang Ao, Fuzhen Zhuang, and Qing He. 2022. User-centric conversational recommendation with multi-aspect user modeling. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 223--233.Google ScholarDigital Library
- Qi Liu, Matt Kusner, and Phil Blunsom. 2021b. Counterfactual data augmentation for neural machine translation. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 187--197.Google ScholarCross Ref
- Zhiwei Liu, Ziwei Fan, Yu Wang, and Philip S Yu. 2021a. Augmenting sequential recommendation with pseudo-prior items via reversely pre-training transformer. In Proceedings of the 44th international ACM SIGIR conference on Research and development in information retrieval. 1608--1612.Google ScholarDigital Library
- Zeming Liu, Haifeng Wang, Zheng-Yu Niu, Hua Wu, Wanxiang Che, and Ting Liu. 2020. Towards Conversational Recommendation over Multi-Type Dialogs. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 1036--1049.Google ScholarCross Ref
- Ilya Loshchilov and Frank Hutter. 2018. Decoupled Weight Decay Regularization. In ICLR.Google Scholar
- Yu Lu, Junwei Bao, Yan Song, Zichen Ma, Shuguang Cui, Youzheng Wu, and Xiaodong He. 2021. RevCore: Review-Augmented Conversational Recommendation. In ACL Findings. 1161--1173.Google Scholar
- S Chandra Mouli, Yangze Zhou, and Bruno Ribeiro. [n.,d.]. Bias Challenges in Counterfactual Data Augmentation. In UAI 2022 Workshop on Causal Representation Learning.Google Scholar
- Jiao Ou, Jinchao Zhang, Yang Feng, and Jie Zhou. 2022. Counterfactual Data Augmentation via Perspective Transition for Open-Domain Dialogues. arXiv preprint arXiv:2210.16838 (2022).Google Scholar
- Silviu Pitis, Elliot Creager, and Animesh Garg. 2020. Counterfactual data augmentation using locally factored dynamics. Advances in Neural Information Processing Systems, Vol. 33 (2020), 3976--3990.Google Scholar
- Ruihong Qiu, Zi Huang, and Hongzhi Yin. 2021. Memory augmented multi-instance contrastive predictive coding for sequential recommendation. In 2021 IEEE International Conference on Data Mining (ICDM). IEEE, 519--528.Google ScholarCross Ref
- Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019. Language models are unsupervised multitask learners. OpenAI blog, Vol. 1, 8 (2019), 9.Google Scholar
- Zhaochun Ren, Zhi Tian, Dongdong Li, Pengjie Ren, Liu Yang, Xin Xin, Huasheng Liang, Maarten de Rijke, and Zhumin Chen. 2022. Variational Reasoning about User Preferences for Conversational Recommendation. In SIGIR.Google Scholar
- Marco Tú lio Ribeiro, Sameer Singh, and Carlos Guestrin. 2018. Semantically Equivalent Adversarial Rules for Debugging NLP models. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, ACL 2018, Melbourne, Australia, July 15-20, 2018, Volume 1: Long Papers. 856--865. https://doi.org/10.18653/v1/P18-1079Google ScholarCross Ref
- Michael Schlichtkrull, Thomas N Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, and Max Welling. 2018. Modeling relational data with graph convolutional networks. In European semantic web conference. Springer, 593--607.Google Scholar
- Chenzhan Shang, Yupeng Hou, Wayne Xin Zhao, Yaliang Li, and Jing Zhang. 2023. Multi-grained Hypergraph Interest Modeling for Conversational Recommendation. arXiv preprint arXiv:2305.04798 (2023).Google Scholar
- Connor Shorten and Taghi M Khoshgoftaar. 2019. A survey on image data augmentation for deep learning. Journal of big data, Vol. 6, 1 (2019), 1--48.Google ScholarCross Ref
- Sandeep Subramanian, Adam Trischler, Yoshua Bengio, and Christopher J Pal. 2018. Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning. In ICLR.Google Scholar
- Lingzhi Wang, Huang Hu, Lei Sha, Can Xu, Daxin Jiang, and Kam-Fai Wong. 2022a. RecInDial: A Unified Framework for Conversational Recommendation with Pretrained Language Models. In Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing. 489--500.Google Scholar
- Ting-Chun Wang, Shang-Yu Su, and Yun-Nung Chen. 2022b. BARCOR: Towards A Unified Framework for Conversational Recommendation Systems. arXiv preprint arXiv:2203.14257 (2022).Google Scholar
- Xiaolei Wang, Xinyu Tang, Wayne Xin Zhao, Jingyuan Wang, and Ji-Rong Wen. 2023. Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Models. arXiv preprint arXiv:2305.13112 (2023).Google Scholar
- Xiaolei Wang, Kun Zhou, Ji-Rong Wen, and Wayne Xin Zhao. 2022c. Towards Unified Conversational Recommender Systems via Knowledge-Enhanced Prompt Learning. arXiv preprint arXiv:2206.09363 (2022).Google Scholar
- Yicheng Wang and Mohit Bansal. 2018. Robust Machine Comprehension Models via Adversarial Training. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT, New Orleans, Louisiana, USA, June 1-6, 2018, Volume 2 (Short Papers). 575--581. https://doi.org/10.18653/v1/n18-2091Google ScholarCross Ref
- Zhenlei Wang, Jingsen Zhang, Hongteng Xu, Xu Chen, Yongfeng Zhang, Wayne Xin Zhao, and Ji-Rong Wen. 2021. Counterfactual data-augmented sequential recommendation. In Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval. 347--356.Google ScholarDigital Library
- Jason W. Wei and Kai Zou. 2019. EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019, Hong Kong, China, November 3-7, 2019. 6381--6387. https://doi.org/10.18653/v1/D19-1670Google Scholar
- Qingsong Wen, Liang Sun, Fan Yang, Xiaomin Song, Jingkun Gao, Xue Wang, and Huan Xu. 2020. Time series data augmentation for deep learning: A survey. arXiv preprint arXiv:2002.12478 (2020).Google Scholar
- Ronald J Williams. 1992. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Reinforcement learning (1992), 5--32.Google Scholar
- Qizhe Xie, Zihang Dai, Eduard H. Hovy, Thang Luong, and Quoc Le. 2020. Unsupervised Data Augmentation for Consistency Training. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual. https://proceedings.neurips.cc/paper/2020/hash/44feb0096faa8326192570788b38c1d1-Abstract.htmlGoogle Scholar
- Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz. 2017. mixup: Beyond empirical risk minimization. arXiv preprint arXiv:1710.09412 (2017).Google Scholar
- Xiaoyu Zhang, Xin Xin, Dongdong Li, Wenxuan Liu, Pengjie Ren, Zhumin Chen, Jun Ma, and Zhaochun Ren. 2022. Variational Reasoning over Incomplete Knowledge Graphs for Conversational Recommendation. arXiv preprint arXiv:2212.11868 (2022).Google Scholar
- Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, and William B Dolan. 2020. DIALOGPT: Large-Scale Generative Pre-training for Conversational Response Generation. In ACL. 270--278.Google Scholar
- Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, et al. 2023. A survey of large language models. arXiv preprint arXiv:2303.18223 (2023).Google Scholar
- Jinfeng Zhou, Bo Wang, Zhitong Yang, Dongming Zhao, Kun Huang, Ruifang He, and Yuexian Hou. 2022a. CR-GIS: Improving Conversational Recommendation via Goal-aware Interest Sequence Modeling. In Proceedings of the 29th International Conference on Computational Linguistics. 400--411.Google Scholar
- Kun Zhou, Xiaolei Wang, Yuanhang Zhou, Chenzhan Shang, Yuan Cheng, Wayne Xin Zhao, Yaliang Li, and Ji-Rong Wen. 2021. CRSLab: An Open-Source Toolkit for Building Conversational Recommender System. In ACL: System Demonstrations. 185--193.Google Scholar
- Kun Zhou, Wayne Xin Zhao, Shuqing Bian, Yuanhang Zhou, Ji-Rong Wen, and Jingsong Yu. 2020a. Improving conversational recommender systems via knowledge graph based semantic fusion. In KDD. 1006--1014.Google Scholar
- Kun Zhou, Wayne Xin Zhao, Hui Wang, Sirui Wang, Fuzheng Zhang, Zhongyuan Wang, and Ji-Rong Wen. 2020b. Leveraging historical interaction data for improving conversational recommender system. In CIKM. 2349--2352.Google Scholar
- Kun Zhou, Yuanhang Zhou, Wayne Xin Zhao, Xiaoke Wang, and Ji-Rong Wen. 2020c. Towards Topic-Guided Conversational Recommender System. In Proceedings of the 28th International Conference on Computational Linguistics. 4128--4139.Google ScholarCross Ref
- Yuanhang Zhou, Kun Zhou, Wayne Xin Zhao, Cheng Wang, Peng Jiang, and He Hu. 2022b. C(2)-CRS: Coarse-to-Fine Contrastive Learning for Conversational Recommender System. In WSDM 2022. ACM, 1488--1496.Google ScholarDigital Library
- Ran Zmigrod, Sabrina J Mielke, Hanna Wallach, and Ryan Cotterell. 2019. Counterfactual Data Augmentation for Mitigating Gender Stereotypes in Languages with Rich Morphology. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 1651--1661.Google ScholarCross Ref
- Jie Zou, Evangelos Kanoulas, Pengjie Ren, Zhaochun Ren, Aixin Sun, and Cheng Long. 2022. Improving Conversational Recommender Systems via Transformer-based Sequential Modelling. In SIGIR. 2319--2324.Google Scholar
Index Terms
- Improving Conversational Recommendation Systems via Counterfactual Data Simulation
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