C2-CRS: Coarse-to-Fine Contrastive Learning for Conversational Recommender System

01/04/2022
by   Yuanhang Zhou, et al.
9

Conversational recommender systems (CRS) aim to recommend suitable items to users through natural language conversations. For developing effective CRSs, a major technical issue is how to accurately infer user preference from very limited conversation context. To address issue, a promising solution is to incorporate external data for enriching the context information. However, prior studies mainly focus on designing fusion models tailored for some specific type of external data, which is not general to model and utilize multi-type external data. To effectively leverage multi-type external data, we propose a novel coarse-to-fine contrastive learning framework to improve data semantic fusion for CRS. In our approach, we first extract and represent multi-grained semantic units from different data signals, and then align the associated multi-type semantic units in a coarse-to-fine way. To implement this framework, we design both coarse-grained and fine-grained procedures for modeling user preference, where the former focuses on more general, coarse-grained semantic fusion and the latter focuses on more specific, fine-grained semantic fusion. Such an approach can be extended to incorporate more kinds of external data. Extensive experiments on two public CRS datasets have demonstrated the effectiveness of our approach in both recommendation and conversation tasks.

READ FULL TEXT

Authors

page 1

page 2

page 3

page 4

07/08/2020

Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusion

Conversational recommender systems (CRS) aim to recommend high-quality i...
06/19/2022

Towards Unified Conversational Recommender Systems via Knowledge-Enhanced Prompt Learning

Conversational recommender systems (CRS) aim to proactively elicit user ...
04/11/2019

Sound, Fine-Grained Traversal Fusion for Heterogeneous Trees - Extended Version

Applications in many domains are based on a series of traversals of tree...
03/20/2022

Multi-view Multi-behavior Contrastive Learning in Recommendation

Multi-behavior recommendation (MBR) aims to jointly consider multiple be...
08/19/2020

Leveraging Historical Interaction Data for Improving Conversational Recommender System

Recently, conversational recommender system (CRS) has become an emerging...
02/04/2014

Topic Segmentation and Labeling in Asynchronous Conversations

Topic segmentation and labeling is often considered a prerequisite for h...
06/03/2021

AliCG: Fine-grained and Evolvable Conceptual Graph Construction for Semantic Search at Alibaba

Conceptual graphs, which is a particular type of Knowledge Graphs, play ...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.