TREA: Tree-Structure Reasoning Schema for Conversational Recommendation

• URL: http://arxiv.org/abs/2307.10543v1
• Date: Thu, 20 Jul 2023 02:48:04 GMT
• Title: TREA: Tree-Structure Reasoning Schema for Conversational Recommendation
• Authors: Wendi Li, Wei Wei, Xiaoye Qu, Xian-Ling Mao, Ye Yuan, Wenfeng Xie, Dangyang Chen
• Abstract summary: We propose a novel Tree structure Reasoning schEmA named TREA. TREA constructs a multi-archhierical tree as the reasoning structure to clarify the causal relationships between mentioned entities. Extensive experiments on two public CRS datasets have demonstrated the effectiveness of our approach.
• Score: 23.29064805769382
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Conversational recommender systems (CRS) aim to timely trace the dynamic interests of users through dialogues and generate relevant responses for item recommendations. Recently, various external knowledge bases (especially knowledge graphs) are incorporated into CRS to enhance the understanding of conversation contexts. However, recent reasoning-based models heavily rely on simplified structures such as linear structures or fixed-hierarchical structures for causality reasoning, hence they cannot fully figure out sophisticated relationships among utterances with external knowledge. To address this, we propose a novel Tree structure Reasoning schEmA named TREA. TREA constructs a multi-hierarchical scalable tree as the reasoning structure to clarify the causal relationships between mentioned entities, and fully utilizes historical conversations to generate more reasonable and suitable responses for recommended results. Extensive experiments on two public CRS datasets have demonstrated the effectiveness of our approach.

Related papers

• Leveraging Inter-Chunk Interactions for Enhanced Retrieval in Large Language Model-Based Question Answering [12.60063463163226]
  IIER captures the internal connections between document chunks by considering three types of interactions: structural, keyword, and semantic. It identifies multiple seed nodes based on the target question and iteratively searches for relevant chunks to gather supporting evidence. It refines the context and reasoning chain, aiding the large language model in reasoning and answer generation.
  arXiv  Detail & Related papers  (2024-08-06T02:39:55Z)
• Knowledge Graphs and Pre-trained Language Models enhanced Representation Learning for Conversational Recommender Systems [58.561904356651276]
  We introduce the Knowledge-Enhanced Entity Representation Learning (KERL) framework to improve the semantic understanding of entities for Conversational recommender systems. KERL uses a knowledge graph and a pre-trained language model to improve the semantic understanding of entities. KERL achieves state-of-the-art results in both recommendation and response generation tasks.
  arXiv  Detail & Related papers  (2023-12-18T06:41:23Z)
• Conversational Recommendation as Retrieval: A Simple, Strong Baseline [4.737923227003888]
  Conversational recommendation systems (CRS) aim to recommend suitable items to users through natural language conversation. Most CRS approaches do not effectively utilize the signal provided by these conversations. We propose an alternative information retrieval (IR)-styled approach to the CRS item recommendation task.
  arXiv  Detail & Related papers  (2023-05-23T06:21:31Z)
• Query Structure Modeling for Inductive Logical Reasoning Over Knowledge Graphs [67.043747188954]
  We propose a structure-modeled textual encoding framework for inductive logical reasoning over KGs. It encodes linearized query structures and entities using pre-trained language models to find answers. We conduct experiments on two inductive logical reasoning datasets and three transductive datasets.
  arXiv  Detail & Related papers  (2023-05-23T01:25:29Z)
• Variational Reasoning over Incomplete Knowledge Graphs for Conversational Recommendation [48.70062671767362]
  We propose the Variational Reasoning over Incomplete KGs Conversational Recommender (VRICR) Our key idea is to incorporate the large dialogue corpus naturally accompanied with CRSs to enhance the incomplete KGs. We also denote the dialogue-specific subgraphs of KGs as latent variables with categorical priors for adaptive knowledge graphs.
  arXiv  Detail & Related papers  (2022-12-22T17:02:21Z)
• Structure Extraction in Task-Oriented Dialogues with Slot Clustering [94.27806592467537]
  In task-oriented dialogues, dialogue structure has often been considered as transition graphs among dialogue states. We propose a simple yet effective approach for structure extraction in task-oriented dialogues.
  arXiv  Detail & Related papers  (2022-02-28T20:18:12Z)
• Speaker-Oriented Latent Structures for Dialogue-Based Relation Extraction [10.381257436462116]
  We introduce SOLS, a novel model which can explicitly induce speaker-oriented latent structures for better DiaRE. Specifically, we learn latent structures to capture the relationships among tokens beyond the utterance boundaries. During the learning process, our speaker-specific regularization method progressively highlights speaker-related key clues and erases the irrelevant ones.
  arXiv  Detail & Related papers  (2021-09-11T04:24:51Z)
• CR-Walker: Tree-Structured Graph Reasoning and Dialog Acts for Conversational Recommendation [62.13413129518165]
  CR-Walker is a model that performs tree-structured reasoning on a knowledge graph. It generates informative dialog acts to guide language generation. Automatic and human evaluations show that CR-Walker can arrive at more accurate recommendation.
  arXiv  Detail & Related papers  (2020-10-20T14:53:22Z)
• Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusion [77.21442487537139]
  Conversational recommender systems (CRS) aim to recommend high-quality items to users through interactive conversations. First, the conversation data itself lacks of sufficient contextual information for accurately understanding users' preference. Second, there is a semantic gap between natural language expression and item-level user preference.
  arXiv  Detail & Related papers  (2020-07-08T11:14:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.