Conversational Recommendation via Hierarchical Information Modeling

Tu, Quan; Gao, Shen; Li, Yanran; Cui, Jianwei; Wang, Bin; Yan, Rui

doi:10.1145/3477495.3531830

Cited by 7 publications

(6 citation statements)

References 26 publications

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“…Conversational Recommender Systems (CRSs) are designed to provide item recommendations through multi-turn interaction. The interaction can be divided into two main categories: question answering based on templates (Lei et al, 2020;Tu et al, 2022) and chit-chat based on natural language (Wang et al, 2023;Zhao et al, 2023c). In this work, we consider the second category.…”

Section: Task Descriptionmentioning

confidence: 99%

“…One popular approach (Lei et al, 2020;Tu et al, 2022) assumes that interactions with users primarily take the form of question answering, where users are asked about their preferences for items and their attributes. The goal is to learn an optimal interaction strategy that captures user preferences and provides accurate recommendations in as few turns as possible.…”

Section: B1 Conversational Recommender Systemmentioning

confidence: 99%

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Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Models

Wang,

Tang,

Zhao

et al. 2023

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

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The recent success of large language models (LLMs) has shown great potential to develop more powerful conversational recommender systems (CRSs), which rely on natural language conversations to satisfy user needs. In this paper, we embark on an investigation into the utilization of ChatGPT for CRSs, revealing the inadequacy of the existing evaluation protocol. It might overemphasize the matching with ground-truth items annotated by humans while neglecting the interactive nature of CRSs.To overcome the limitation, we further propose an interactive Evaluation approach based on LLMs, named iEvaLM, which harnesses LLM-based user simulators. Our evaluation approach can simulate various system-user interaction scenarios. Through the experiments on two public CRS datasets, we demonstrate notable improvements compared to the prevailing evaluation protocol. Furthermore, we emphasize the evaluation of explainability, and Chat-GPT showcases persuasive explanation generation for its recommendations. Our study contributes to a deeper comprehension of the untapped potential of LLMs for CRSs and provides a more flexible and realistic evaluation approach for future research about LLMbased CRSs. The code is available at https: //github.com/RUCAIBox/iEvaLM-CRS.

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Section: Task Descriptionmentioning

confidence: 99%

Section: B1 Conversational Recommender Systemmentioning

confidence: 99%

Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Models

Wang,

Tang,

Zhao

et al. 2023

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

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“…I'm glad I could provide some assistance. and HICR [36] further leverage (knowledge) graphs to guide the generation of the asked features or recommended items. Recently, to further foster the scalability and generality of CRSs, UNICORN [5] and CRIF [10] replace the separated conversation and recommendation components in previous studies with a unified policy learning process.…”

Section: Do You Like Pink and Gray Style?mentioning

confidence: 99%

Towards Building Voice-based Conversational Recommender Systems: Datasets, Potential Solutions and Prospects

Qu¹,

Li²,

Sun

et al. 2023

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

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Conversational recommender systems (CRSs) have become crucial emerging research topics in the field of RSs, thanks to their natural advantages of explicitly acquiring user preferences via interactive conversations and revealing the reasons behind recommendations. However, the majority of current CRSs are text-based, which is less user-friendly and may pose challenges for certain users, such as those with visual impairments or limited writing and reading abilities. Therefore, for the first time, this paper investigates the potential of voice-based CRS (VCRSs) to revolutionize the way users interact with RSs in a natural, intuitive, convenient, and accessible fashion.To support such studies, we create two VCRSs benchmark datasets in the e-commerce and movie domains, after realizing the lack of such datasets through an exhaustive literature review. Specifically, we first empirically verify the benefits and necessity of creating such datasets. Thereafter, we convert the user-item interactions to text-based conversations through the ChatGPT-driven prompts for generating diverse and natural templates, and then synthesize the corresponding audios via the text-to-speech model. Meanwhile, a number of strategies are delicately designed to ensure the naturalness and high quality of voice conversations. On this basis, we further explore the potential solutions and point out possible directions to build end-to-end VCRSs by seamlessly extracting and integrating voice-based inputs, thus delivering performance-enhanced,

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“…The goal of CRS models is to offer personalized recommendations via interactive dialogues. One line of CRS methods (Lei et al, 2020a,b;Ren et al, 2021;Deng et al, 2021;Tu et al, 2022) mainly focuses on improving the performance of item recommendation, where they ask clarifying questions to gradually find an optimal candidate set. Therefore, the quality of generated responses is less emphasized as these works only leverage pre-defined response templates (Lei et al, 2020a,b;Ren et al, 2021) to interact with the users.…”

Section: Conversational Recommender Systemsmentioning

confidence: 99%

“…In recent years, conversational recommender systems (CRSs) (Li et al, 2018;Kang et al, 2019;Zhou et al, 2020b;Zhou et al, 2021b;Li et al, 2022;Ren et al, 2022;Zou et al, 2022;Chu et al, 2023) have gained considerable attention from both academic researchers and industrial practitioners. Inside such systems, most CRS models will first infer user preferences via multi-turn conversations and then recommend a set of potential items when appropriate.…”

Section: Introductionmentioning

confidence: 99%

Reinforced Target-driven Conversational Promotion

Dao,

Liao,

et al. 2023

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

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The ability to proactively engage with users towards pitching products is highly desired for conversational assistants. However, existing conversational recommendation methods overemphasize on acquiring user preferences while ignore the strategic planning for nudging users towards accepting a designated item. Hence, these methods fail to promote specified items with engaging responses. In this work, we propose a Reinforced Target-driven Conversational Promotion (RTCP) framework for conversational promotion. Specifically, RTCP integrates short-term and long-term planning via a balanced gating mechanism. Inside which, the dialogue strategies are predicted via knowledge-integrated multi-head attention and guided via reinforcement learning rewards. RTCP then employs an action-guided prefix tuning method to generate relevant responses. Experimental results demonstrate that our model outperforms state-of-the-art models on both automatic metrics and human evaluation. Moreover, RTCP has a strong capability in quickly adapting to unseen scenarios just by updating prefix parameters without re-training the whole model. Code and data are here 1 .

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Conversational Recommendation via Hierarchical Information Modeling

Cited by 7 publications

References 26 publications

Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Models

Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Models

Towards Building Voice-based Conversational Recommender Systems: Datasets, Potential Solutions and Prospects

Reinforced Target-driven Conversational Promotion

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