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Parameter-Efficient Conversational Recommender System as a Language Processing Task (2401.14194v3)

Published 25 Jan 2024 in cs.CL

Abstract: Conversational recommender systems (CRS) aim to recommend relevant items to users by eliciting user preference through natural language conversation. Prior work often utilizes external knowledge graphs for items' semantic information, a LLM for dialogue generation, and a recommendation module for ranking relevant items. This combination of multiple components suffers from a cumbersome training process, and leads to semantic misalignment issues between dialogue generation and item recommendation. In this paper, we represent items in natural language and formulate CRS as a natural language processing task. Accordingly, we leverage the power of pre-trained LLMs to encode items, understand user intent via conversation, perform item recommendation through semantic matching, and generate dialogues. As a unified model, our PECRS (Parameter-Efficient CRS), can be optimized in a single stage, without relying on non-textual metadata such as a knowledge graph. Experiments on two benchmark CRS datasets, ReDial and INSPIRED, demonstrate the effectiveness of PECRS on recommendation and conversation. Our code is available at: https://github.com/Ravoxsg/efficient_unified_crs.

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References (58)
  1. Dbpedia: A nucleus for a web of open data. page 722–735. Springer-Verlag.
  2. Towards knowledge-based recommender dialog system. 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), pages 1803–1813, Hong Kong, China. Association for Computational Linguistics.
  3. Vision transformer adapter for dense predictions. In The Eleventh International Conference on Learning Representations.
  4. Towards conversational recommender systems. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, page 815–824. Association for Computing Machinery.
  5. A unified multi-task learning framework for multi-goal conversational recommender systems. ACM Trans. Inf. Syst., 41(3).
  6. Qlora: Efficient finetuning of quantized llms. ArXiv, abs/2305.14314.
  7. Hierarchical neural story generation. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 889–898. Association for Computational Linguistics.
  8. Exploring adapter-based transfer learning for recommender systems: Empirical studies and practical insights. ArXiv, abs/2305.15036.
  9. Advances and challenges in conversational recommender systems: A survey. AI Open, 2:100–126.
  10. Michael U. Gutmann and Aapo Hyvärinen. 2012. Noise-contrastive estimation of unnormalized statistical models, with applications to natural image statistics. J. Mach. Learn. Res., 13:307–361.
  11. INSPIRED: Toward sociable recommendation dialog systems. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 8142–8152, Online. Association for Computational Linguistics.
  12. Parameter-efficient model adaptation for vision transformers. ArXiv, abs/2203.16329.
  13. The curious case of neural text degeneration. In International Conference on Learning Representations.
  14. Parameter-efficient transfer learning for NLP. In Proceedings of the 36th International Conference on Machine Learning, volume 97, pages 2790–2799.
  15. Learning to infer user implicit preference in conversational recommendation. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, page 256–266. Association for Computing Machinery.
  16. LoRA: Low-rank adaptation of large language models. In International Conference on Learning Representations.
  17. Llm-adapters: An adapter family for parameter-efficient fine-tuning of large language models. ArXiv, abs/2304.01933.
  18. A survey on conversational recommender systems. ACM Comput. Surv., 54(5).
  19. Estimation–action–reflection: Towards deep interaction between conversational and recommender systems. In WSDM 2020 - Proceedings of the 13th International Conference on Web Search and Data Mining, pages 304–312. Association for Computing Machinery, Inc.
  20. The power of scale for parameter-efficient prompt tuning. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 3045–3059, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
  21. BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 7871–7880, Online. Association for Computational Linguistics.
  22. Towards deep conversational recommendations. In Proceedings of the 32nd International Conference on Neural Information Processing Systems, page 9748–9758. Curran Associates Inc.
  23. 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, page 223–233. Association for Computing Machinery.
  24. Learning neural templates for recommender dialogue system. arXiv preprint arXiv:2109.12302.
  25. Chin-Yew Lin. 2004. ROUGE: A package for automatic evaluation of summaries. In Text Summarization Branches Out, pages 74–81, Barcelona, Spain. Association for Computational Linguistics.
  26. How can recommender systems benefit from large language models: A survey. ArXiv, abs/2306.05817.
  27. Towards conversational recommendation over multi-type dialogs. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 1036–1049, Online. Association for Computational Linguistics.
  28. Graph-grounded goal planning for conversational recommendation. IEEE Transactions on Knowledge and Data Engineering, 35(5):4923–4939.
  29. Ilya Loshchilov and Frank Hutter. 2019. Decoupled weight decay regularization. ArXiv, abs/1711.05101.
  30. RevCore: Review-augmented conversational recommendation. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pages 1161–1173, Online. Association for Computational Linguistics.
  31. Cr-walker: Tree-structured graph reasoning and dialog acts for conversational recommendation. arXiv preprint arXiv:2010.10333.
  32. Andriy Mnih and Koray Kavukcuoglu. 2013. Learning word embeddings efficiently with noise-contrastive estimation. In Advances in Neural Information Processing Systems, volume 26. Curran Associates, Inc.
  33. Andriy Mnih and Yee Whye Teh. 2012. A fast and simple algorithm for training neural probabilistic language models. In Proceedings of the 29th International Coference on International Conference on Machine Learning, page 419–426.
  34. Dhanya Pramod and Prafulla Bafna. 2022. Conversational recommender systems techniques, tools, acceptance, and adoption: A state of the art review. Expert Syst. Appl., 203(C).
  35. Language models are unsupervised multitask learners.
  36. Crsal: Conversational recommender systems with adversarial learning. ACM Trans. Inf. Syst., 38(4).
  37. Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter. ArXiv, abs/1910.01108.
  38. Modeling relational data with graph convolutional networks. In The Semantic Web, pages 593–607. Springer International Publishing.
  39. Conceptnet 5.5: An open multilingual graph of general knowledge. In Proceedings of the AAAI Conference on Artificial Intelligence.
  40. Conversational recommender system. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, page 235–244. Association for Computing Machinery.
  41. Llama: Open and efficient foundation language models. ArXiv, abs/2302.13971.
  42. Diverse beam search: Decoding diverse solutions from neural sequence models. In Proceedings of the AAAI Conference on Artificial Intelligence.
  43. 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 (Volume 1: Long Papers), pages 489–500, Online only. Association for Computational Linguistics.
  44. Barcor: Towards a unified framework for conversational recommendation systems. ArXiv, abs/2203.14257.
  45. Towards unified conversational recommender systems via knowledge-enhanced prompt learning. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, page 1929–1937. Association for Computing Machinery.
  46. Transformers: State-of-the-art natural language processing. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 38–45, Online. Association for Computational Linguistics.
  47. A survey on large language models for recommendation. ArXiv, abs/2305.19860.
  48. Improving conversational recommendation systems’ quality with context-aware item meta-information. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 38–48, Seattle, United States. Association for Computational Linguistics.
  49. Llama-adapter: Efficient fine-tuning of language models with zero-init attention. ArXiv, abs/2303.16199.
  50. Bertscore: Evaluating text generation with bert. arXiv preprint arXiv:1904.09675.
  51. Toward knowledge-enriched conversational recommendation systems. In Proceedings of the 4th Workshop on NLP for Conversational AI, pages 212–217, Dublin, Ireland. Association for Computational Linguistics.
  52. Variational reasoning over incomplete knowledge graphs for conversational recommendation. In Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining. ACM.
  53. DIALOGPT : Large-scale generative pre-training for conversational response generation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pages 270–278, Online. Association for Computational Linguistics.
  54. CRFR: Improving conversational recommender systems via flexible fragments reasoning on knowledge graphs. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 4324–4334, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
  55. Improving conversational recommender systems via knowledge graph based semantic fusion. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, page 1006–1014. Association for Computing Machinery.
  56. Towards topic-guided conversational recommender system. In Proceedings of the 28th International Conference on Computational Linguistics, pages 4128–4139, Barcelona, Spain (Online). International Committee on Computational Linguistics.
  57. C²-crs: Coarse-to-fine contrastive learning for conversational recommender system. In Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining, page 1488–1496. Association for Computing Machinery.
  58. Towards question-based recommender systems. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, page 881–890. Association for Computing Machinery.
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Authors (5)
  1. Mathieu Ravaut (17 papers)
  2. Hao Zhang (947 papers)
  3. Lu Xu (68 papers)
  4. Aixin Sun (99 papers)
  5. Yong Liu (721 papers)
Citations (3)
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  1. GitHub - Ravoxsg/efficient_unified_crs: Source code for PECRS (EACL 2024) (9 stars)
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