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Towards Conversational Recommendation over Multi-Type Dialogs

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arxiv 2005.03954 v3 pith:KY5US56P submitted 2020-05-08 cs.CL cs.AI

Zeming Liu , Haifeng Wang , Zheng-Yu Niu , Hua Wu , Wanxiang Che , Ting Liu This is my paper
classification cs.CLcs.AI
keywords dialogrecommendationdialogsdatasetmulti-typeconversationaldurecdiallead
verification ladder T0 review T1 audit T2 compute T3 formal
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We propose a new task of conversational recommendation over multi-type dialogs, where the bots can proactively and naturally lead a conversation from a non-recommendation dialog (e.g., QA) to a recommendation dialog, taking into account user's interests and feedback. To facilitate the study of this task, we create a human-to-human Chinese dialog dataset \emph{DuRecDial} (about 10k dialogs, 156k utterances), which contains multiple sequential dialogs for every pair of a recommendation seeker (user) and a recommender (bot). In each dialog, the recommender proactively leads a multi-type dialog to approach recommendation targets and then makes multiple recommendations with rich interaction behavior. This dataset allows us to systematically investigate different parts of the overall problem, e.g., how to naturally lead a dialog, how to interact with users for recommendation. Finally we establish baseline results on DuRecDial for future studies. Dataset and codes are publicly available at https://github.com/PaddlePaddle/models/tree/develop/PaddleNLP/Research/ACL2020-DuRecDial.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A Framework for Generating Conversational Recommendation Datasets from Behavioral Interactions

    cs.IR 2025-06 reject novelty 5.0 of 10

    ConvRecStudio generates roughly 38K synthetic multi-turn recommendation dialogs across three domains from historical interactions, and a cross-attention transformer fusing history with dialog beats dialog-only and his...

  2. HyCoRec: Hypergraph-Enhanced Multi-Preference Learning for Alleviating Matthew Effect in Conversational Recommendation

    cs.IR 2026-07 reject novelty 4.0 of 10

    HyCoRec adds multi-hypergraph preference fusion to a conversational recommender and reports higher coverage/lower isolation on REDIAL and TG-REDIAL, but Matthew-effect alleviation is not dynamically evaluated.

  3. Mitigating Matthew Effect: Multi-Hypergraph Boosted Multi-Interest Self-Supervised Learning for Conversational Recommendation

    cs.IR 2026-07 reject novelty 3.0 of 10

    HiCore, a triple-channel multi-hypergraph model with self-supervised learning, reports new state-of-the-art results on four conversational recommendation datasets and lower popularity bias, though its own tables contr...

  4. Optimizing Conversational Product Recommendation via Reinforcement Learning

    cs.IR 2025-06 reject novelty 1.0 of 10

    A position paper sketching how RL (DQN, PPO, RLHF) could optimize conversational product recommendation, without any validation.

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