KERS: A Knowledge-Enhanced Framework for Recommendation Dialog Systems with Multiple Subgoals (2021.findings-emnlp)
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| Challenge: | Existing frameworks for multi-subgoal dialogs require a system to build a social bond with users to gain trust and develop affinity. |
| Approach: | They propose a framework for common knowledge-based multi-subgoal dialogs that divides up conversations with multiple subgoals and propose mechanisms to filter noisy knowledge and to include cleaned knowledge in the dialog response generation process. |
| Outcome: | The proposed framework obtains state-of-the-art results on a DuRecDial dataset in both automatic and human evaluation. |
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| Challenge: | Existing frameworks that only provide information about user preferences can be inaccurate in e-commerce recommender systems. |
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CR-Walker: Tree-Structured Graph Reasoning and Dialog Acts for Conversational Recommendation (2021.emnlp-main)
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| Challenge: | Existing systems that explore user preference through conversational interactions do not exploit the context and knowledge to make accurate recommendations. |
| Approach: | They propose a model that performs tree-structured reasoning on a knowledge graph and generates informative dialog acts to guide language generation. |
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Suggest me a movie for tonight: Leveraging Knowledge Graphs for Conversational Recommendation (2020.coling-main)
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| Challenge: | Recent studies show that knowledge graphs are incomplete since they do not contain all factual information present on the web. |
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CRFR: Improving Conversational Recommender Systems via Flexible Fragments Reasoning on Knowledge Graphs (2021.emnlp-main)
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Towards Conversational Recommendation over Multi-Type Dialogs (2020.acl-main)
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| Challenge: | In recent years, there has been a significant increase in the work of conversational recommendation due to the rise of voice-based bots. |
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| Challenge: | Knowledge-enhanced Dialogue Systems (KEDS) are a new approach to enhancing human-machine interaction through natural language. |
| Approach: | This tutorial provides an in-depth exploration of Knowledge-enhanced Dialogue Systems (KEDS) it aims to elucidate their significance, highlight advances made using deep learning, and pinpoint the current challenges. |
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Generative Subgraph Retrieval for Knowledge Graph–Grounded Dialog Generation (2024.emnlp-main)
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| Challenge: | Existing methods for knowledge graph–grounded dialog generation fail to leverage the rich knowledge of pretrained language models. |
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| Challenge: | Existing methods for enhancing recommendation quality face false negatives . only one "silly cop movie" is labeled as positive, leading to suboptimal recommendations . |
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Learning Neural Templates for Recommender Dialogue System (2021.emnlp-main)
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Zujie Liang, Huang Hu, Can Xu, Jian Miao, Yingying He, Yining Chen, Xiubo Geng, Fan Liang, Daxin Jiang
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RecInDial: A Unified Framework for Conversational Recommendation with Pretrained Language Models (2022.aacl-main)
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| Challenge: | Existing generative methods to recommend items are shallowly integrated into the model training and have poor chit-chat ability. |
| Approach: | They propose a framework that integrates recommendation into the dialog generation by introducing a vocabulary pointer. |
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