BARCOR: Towards A Unified Framework for Conversational Recommendation Systems
Recommendation systems focus on helping users find items of interest in the situations of information overload, where users' preferences are typically estimated by the past observed behaviors. In contrast, conversational recommendation systems (CRS) aim to understand users' preferences via interactions in conversation flows. CRS is a complex problem that consists of two main tasks: (1) recommendation and (2) response generation. Previous work often tried to solve the problem in a modular manner, where recommenders and response generators are separate neural models. Such modular architectures often come with a complicated and unintuitive connection between the modules, leading to inefficient learning and other issues. In this work, we propose a unified framework based on BART for conversational recommendation, which tackles two tasks in a single model. Furthermore, we also design and collect a lightweight knowledge graph for CRS in the movie domain. The experimental results show that the proposed methods achieve the state-of-the-art performance in terms of both automatic and human evaluation.
Code (0)
등록된 구현이 없습니다.
Tasks
Conversational RecommendationRecommendation SystemsResponse GenerationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
BARCOR: Towards A Unified Framework for Conversational Recommendation
Recommendation systems focus on helping users find items of interest in the situations of information overload, where users' preferences are typically estimated by past observed behaviors. In contrast, conversational rec…
Conversational RecommendationRecommendation SystemsResponse GenerationLending Interaction Wings to Recommender Systems with Conversational Agents
Recommender systems trained on offline historical user behaviors are embracing conversational techniques to online query user preference. Unlike prior conversational recommendation approaches that systemically combine co…
AttributeConversational RecommendationLanguage ModellingLarge Language Model+1A Unified Multi-task Learning Framework for Multi-goal Conversational Recommender Systems
Recent years witnessed several advances in developing multi-goal conversational recommender systems (MG-CRS) that can proactively attract users' interests and naturally lead user-engaged dialogues with multiple conversat…
Multi-Task LearningRecommendation SystemsResponse GenerationLaViC: Adapting Large Vision-Language Models to Visually-Aware Conversational Recommendation
Conversational recommender systems engage users in dialogues to refine their needs and provide more personalized suggestions. Although textual information suffices for many domains, visually driven categories such as fas…
Conversational RecommendationRecommendation SystemsSelf-Supervised Bot Play for Conversational Recommendation with Justifications
Conversational recommender systems offer the promise of interactive, engaging ways for users to find items they enjoy. We seek to improve conversational recommendation via three dimensions: 1) We aim to mimic a common mo…
Conversational RecommendationRecommendation Systems