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IAI MovieBot 2.0: An Enhanced Research Platform with Trainable Neural Components and Transparent User Modeling (2403.00520v1)
Published 1 Mar 2024 in cs.IR
Abstract: While interest in conversational recommender systems has been on the rise, operational systems suitable for serving as research platforms for comprehensive studies are currently lacking. This paper introduces an enhanced version of the IAI MovieBot conversational movie recommender system, aiming to evolve it into a robust and adaptable platform for conducting user-facing experiments. The key highlights of this enhancement include the addition of trainable neural components for natural language understanding and dialogue policy, transparent and explainable modeling of user preferences, along with improvements in the user interface and research infrastructure.
- UserSimCRS: A User Simulation Toolkit for Evaluating Conversational Recommender Systems. In Proc. of WSDM ’23. 1160–1163.
- Transparent, Scrutable and Explainable User Models for Personalized Recommendation. In Proc. of SIGIR’19. 265–274.
- BERT for Joint Intent Classification and Slot Filling. arXiv:1902.10909 [cs]
- Vote Goat: Conversational Movie Recommendation. In Proc. of SIGIR ’18. 1285–1288.
- Advances and challenges in conversational recommender systems: A survey. AI Open 2 (2021), 100–126.
- Neural approaches to conversational AI. In Proc. of SIGIR ’18. 1371–1374.
- IAI MovieBot: A Conversational Movie Recommender System. In Proc. of CIKM ’20. 3405–3408.
- A Survey on Conversational Recommender Systems. ACM Comput. Surv. 54, 5 (2021).
- Vijay Konda and John Tsitsiklis. 1999. Actor-Critic Algorithms. In Advances in Neural Information Processing Systems (NIPS ’19).
- DAGFiNN: A Conversational Conference Assistant. In Proc. of RecSys ’22. 628–631.
- Soliciting User Preferences in Conversational Recommender Systems via Usage-Related Questions. In Proc. of RecSys ’21. 724–729.
- Human-level control through deep reinforcement learning. Nature 518 (2015), 529–533.
- Yueming Sun and Yi Zhang. 2018. Conversational Recommender System. In Proc. of SIGIR ’18. 235–244.
- Gymnasium. https://zenodo.org/record/8127025 Accessed: 2023-09-15.
- A survey of joint intent detection and slot-filling models in natural language understanding. arXiv:2101.08091 [cs]
- Shuo Zhang and Krisztian Balog. 2020. Evaluating Conversational Recommender Systems via User Simulation. In Proc. of KDD ’20. 1512–1520.
- Analyzing and Simulating User Utterance Reformulation in Conversational Recommender Systems. In Proc. of SIGIR ’22. 133–143.
- Towards Conversational Search and Recommendation: System Ask, User Respond. In Proc. of CIKM ’18. 177–186.
- CRSLab: An Open-Source Toolkit for Building Conversational Recommender System. arXiv:2101.00939 [cs.CL]
- ConvLab-3: A Flexible Dialogue System Toolkit Based on a Unified Data Format. arXiv:2211.17148 [cs.CL]