Towards Knowledge-Based Recommender Dialog System

By Qibin Chen, Junyang Lin, Yichang Zhang, Ming Ding, Yukuo Cen, Hongxia Yang, Jie Tang. Conference on Empirical Methods in Natural Language Processing, 2019

310 citations recorded by the source corpus.

In this paper, we propose a novel end-to-end framework called KBRD, which stands for Knowledge-Based Recommender Dialog System. It integrates the recommender system and the dialog generation system. The dialog generation system can enhance the performance of the recommendation system by introducing information about users’ preferences, and the recommender system can improve that of the dialog generation system by providing recommendation-aware vocabulary bias. Experimental results demonstrate that our proposed model has significant advantages over the baselines in both the evaluation of dialog generation and recommendation. A series of analyses show that the two systems can bring mutual benefits to each other, and the introduced knowledge contributes to both their performances.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/42ffd3a1ab4a139c5ec33b50bd7e5759077f03c4

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Citations in the corpus

Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach (2023, unknown): “To enhance the readability, we make some modifications to the original instructions that are used in our experiments.”
A Survey on Conversational Recommender Systems (2020, methodology): “, with respect to fluency, appropriateness, consistency, engagingness, relevance, informativeness, and the overall dialogue quality and generation performance [27, 45, 63, 74, 75, 100, 146].”
Advances and Challenges in Conversational Recommender Systems: A Survey (2021, methodology): “Generally, interactive recommendations [231, 175, 204, 40], critiquing methods [23, 187, 107, 106], and CRSs focusing on the multi-turn conversation strategy [32, 31, 88, 89, 95] are prone to use the pre-annotated input and rule-based or templatebased output; dialogue systems [200, 220, 51] and CRSs”
Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusion (2020, methodology): “sible to generate the text for explaining the recommendation, e.g., “thriller movie with good plot”. For enriching the conversation information, external knowledge graph (KG) has been utilized in CRS [4]. They mainly focus on incorporating item knowledge, while the word-level enrichment (e.g., the r”
CRSLab: An Open-Source Toolkit for Building Conversational Recommender System (2021, methodology): “So far, we have incorporated 6 commonly-used human-annotated datasets and implemented 18 models, including advanced techniques such as graph neural network (GNN) (Schlichtkrull et al., 2018; Zhou et al., 2020a) and pre-training models (Devlin et al., 2019; Zhou et al., 2020b).”