Skip to main content

Advertisement

Springer Nature Link
Log in

Conversation and recommendation: knowledge-enhanced personalized dialog system

  • Regular Paper
  • Published:
Knowledge and Information Systems Aims and scope Submit manuscript

Abstract

Traditional recommender systems are usually single-shot systems, lacking real-time dialog with customers. Using dialog as an interactive method can help capture user preferences more accurately and enhance system transparency. However, developing such a goal-oriented dialog system has suffered many challenges as the system must collaborate with other subtasks, such as collecting user demands through interaction and recommending appropriate products to users. Additionally, most previous studies on dialog systems do not consider this situation and its challenges. This paper proposes a novel memory network framework for conversational recommendation, which harnesses dialog historical information to endow our model with adaptability in various dialog scenarios. Additionally, it leverages the knowledge base and user profiles to reweight candidates, reducing the ambiguity during interactions and improving the quality of conversational recommender systems. We demonstrate that the proposed method can achieve state-of-the-art performance in a few traditional tasks, such as options display and information provision, through experiments on the personalized bAbI dialog dataset and restaurant recommendation application.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Banchs RE, Li H (2012) Iris: a chat-oriented dialogue system based on the vector space model. In: ACL, pp 37–42

  2. Bi K, Ai Q, Zhang Y, Bruce Croft W (2019) Conversational product search based on negative feedback. In: CIKM, pp 359–368

  3. Bordes A, Lan Boureau Y, Weston J (2017) Learning end-to-end goal-oriented dialog. In: ICLR, pp 1–15

  4. Eric M, Krishnan L, Charette F, Manning CD (2017) Key-value retrieval networks for task-oriented dialogue. In: SIGDIAL, pp 37–49

  5. Gangi Reddy R, Contractor D, Raghu D, Joshi S (2019) Multi-level memory for task oriented dialogs. In: ACL, pp 3744–3754

  6. Gao C, Lei W, He X, de Rijke M, Chua TS (2021) Advances and challenges in conversational recommender systems: a survey. AI Open 2:100–126

    Article  Google Scholar 

  7. Gao J, Galley M, Li L (2018) Neural approaches to conversational AI. In: ACL, pp 2–7

  8. He X, Deng K, Wang X, Li Y, Zhang YD, Wang M (2020) LightGCN: simplifying and powering graph convolution network for recommendation. In: SIGIR, pp 639–648

  9. Huang J, Zhao WX, Dou H, Wen JR, Chang EY (2018) Improving sequential recommendation with knowledge-enhanced memory networks. In: SIGIR, pp 505–514

  10. Iyer RR, Kanumala P, Guo S, Achan K (2020) An end-to-end ml system for personalized conversational voice models in Walmart e-commerce. arXiv preprint arXiv:2011.00866

  11. Joshi CK, Mi F, Faltings B (2017) Personalization in goal-oriented dialog. In: NIPS, pp 2440–2448

  12. Lei W, He X, Miao Y, Wu Q, Hong R, Kan MY, Chua TS (2020) Estimation-action-reflection: towards deep interaction between conversational and recommender systems. In: WSDM, pp 304–312

  13. Lei W, Zhang G, He X, Miao Y, Wang X, Chen L, Chua TS (2020) Interactive path reasoning on graph for conversational recommendation. In: Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, pp 2073–2083

  14. Li J, Monroe W, Ritter A, Galley M, Gao J, Jurafsky D (2016) Deep reinforcement learning for dialogue generation. In: EMNLP, pp 1192–1202

  15. Lin Y, Liu Z, Sun M, Liu Y, Zhu X (2015) Learning entity and relation embeddings for knowledge graph completion. In: AAAI, pp 2181–2187

  16. Lipton Z, Li X, Gao J, Li L, Ahmed F, Deng L (2018) BBQ-networks: efficient exploration in deep reinforcement learning for task-oriented dialogue systems. In: AAAI, pp 5237–5244

  17. Liu F, Perez J (2017) Gated end-to-end memory networks. In: EACL, pp 1–10

  18. Luan Y, Brockett C, Dolan B, Gao J, Galley M (2017) Multi-task learning for speaker-role adaptation in neural conversation models. In: Proceedings of the eighth international joint conference on natural language processing. Asian Federation of Natural Language Processing, Taipei, Taiwan, pp 605–614

  19. Luo L, Huang W, Zeng Q, Nie Z, Sun X (2019) Learning personalized end-to-end goal-oriented dialog. In: AAAI, pp 6794–6801

  20. Maziad H, Rammouz JA, Asmar BE, Tekli J (2021) Preprocessing techniques for end-to-end trainable RNN-based conversational system. In: International conference on web engineering. Springer, pp 255–270

  21. Miller AH, Fisch A, Dodge J, Karimi AH, Bordes A, Weston J (2016) Key-value memory networks for directly reading documents. In: EMNLP, vol 2016, pp 1400–1409

  22. Ren X, Yin H, Chen T, Wang H, Huang Z, Zheng K (2021) Learning to ask appropriate questions in conversational recommendation. In: Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval, pp 808–817

  23. Sarnobat A, Kalola D (2019) A survey on recommender systems. In: IJSRP, p p9356

  24. Sarwar B, Karypis G, Konstan J, Riedl J (2001) Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th international conference on World Wide Web, pp 285–295

  25. Su FG, Hsu AR, Tuan YL, Lee HY (2019) Personalized dialogue response generation learned from monologues. In: Interspeech, pp 4160–4164

  26. Sun Y, Yuan NJ, Wang Y, Xie X, McDonald K, Zhang R (2016) Contextual intent tracking for personal assistants. In: SIGKDD, pp 273–282

  27. Sun Y, Zhang Y (2018) Conversational recommender system. In: The 41st international ACM SIGIR conference on research & development in information retrieval, pp 235–244

  28. Tian X, Hao Y, Zhao P, Wang D, Liu Y, Sheng VS (2021) Considering interaction sequence of historical items for conversational recommender system. In: International conference on database systems for advanced applications, pp 115–131. Springer

  29. Tsumita D, Takagi T (2019) Dialogue based recommender system that flexibly mixes utterances and recommendations. In: WI, pp 51–58

  30. Vaswani A, Brain G, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I (2017) Attention is all you need. In: NIPS, pp 5998–6008

  31. Wang D, Jojic N, Brockett C, Nyberg E (2017) Steering output style and topic in neural response generation. In: EMNLP, pp 2140–2150

  32. Wang Z, Zhang J, Feng J, Chen Z (2014) Knowledge graph embedding by translating on hyperplanes. In: AAAI, pp 1112–1119

  33. Wen TH, Vandyke D, Mrkšíc N, Gašíc M, Rojas-Barahona LM, Su PH, Ultes S, Young S (2017) A network-based end-to-end trainable task-oriented dialogue system. In: EACL, pp 438–449

  34. Wu Y, Wei F, Huang S, Wang Y, Li Z, Zhou M (2019) Response generation by context-aware prototype editing. In: AAAI, pp 7281–7288

  35. Xing C, Wu W, Wu Y, Liu J, Huang Y, Zhou M, Ma WY (2017) Topic aware neural response generation. In: AAAI, pp 3351–3357

  36. Yang L, Hu J, Qiu M, Qu C, Gao J, Bruce Croft W, Liu X, Shen Y, Liu J (2019) A hybrid retrieval-generation neural conversation model. In: CIKM, pp 1341–1350

  37. Yang L, Qiu M, Qu C, Guo J, Zhang Y, Croft WB, Huang J, Chen H (2018) Response ranking with deep matching networks and external knowledge in information-seeking conversation systems. In: SIGIR, pp 245–254

  38. Zhang B, Titov I, Sennrich R (2019) Improving deep transformer with depth-scaled initialization and merged attention. 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)

  39. Zhang Y, Chen X, Ai Q, Yang L, Bruce Croft W (2018) Towards conversational search and recommendation: system Ask, user respond. In: CIKM, pp 177–186

  40. Zhang Z, Takanobu R, Huang M, Zhu X (2020) Recent advances and challenges in task-oriented dialog system. arXiv:2003.07490

  41. Zhao T, Eskenazi M (2016) Towards end-to-end learning for dialog state tracking and management using deep reinforcement learning. In: SIGDIAL, pp 1–10

  42. Zhou K, Zhao WX, Bian S, Zhou Y, Wen JR, Yu J (2020) Improving conversational recommender systems via knowledge graph based semantic fusion. In: Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, pp 1006–1014

  43. Zhou K, Zhao WX, Wang H, Wang S, Zhang F, Wang Z, Wen JR (2020) Leveraging historical interaction data for improving conversational recommender system. In: Proceedings of the 29th ACM international conference on information & knowledge management, pp 2349–2352

Download references

Acknowledgements

This work is supported in part by the Beijing Natural Science Foundation under grant 4192008.

Author information

Authors and Affiliations

  1. Beijing University of Technology, Beijing, 100124, China

    Ming He, Jiwen Wang, Tianyu Ding & Tong Shen

Authors
  1. Ming He
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Jiwen Wang
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Tianyu Ding
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Tong Shen
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Ming He.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

He, M., Wang, J., Ding, T. et al. Conversation and recommendation: knowledge-enhanced personalized dialog system. Knowl Inf Syst 65, 261–279 (2023). https://doi.org/10.1007/s10115-022-01766-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10115-022-01766-6

Keywords