Improving Conversational Recommendation System by Pretraining on Billions Scale of Knowledge Graph

by   Chi-Man Wong, et al.

Conversational Recommender Systems (CRSs) in E-commerce platforms aim to recommend items to users via multiple conversational interactions. Click-through rate (CTR) prediction models are commonly used for ranking candidate items. However, most CRSs are suffer from the problem of data scarcity and sparseness. To address this issue, we propose a novel knowledge-enhanced deep cross network (K-DCN), a two-step (pretrain and fine-tune) CTR prediction model to recommend items. We first construct a billion-scale conversation knowledge graph (CKG) from information about users, items and conversations, and then pretrain CKG by introducing knowledge graph embedding method and graph convolution network to encode semantic and structural information respectively.To make the CTR prediction model sensible of current state of users and the relationship between dialogues and items, we introduce user-state and dialogue-interaction representations based on pre-trained CKG and propose K-DCN.In K-DCN, we fuse the user-state representation, dialogue-interaction representation and other normal feature representations via deep cross network, which will give the rank of candidate items to be recommended.We experimentally prove that our proposal significantly outperforms baselines and show it's real application in Alime.



page 1

page 5


Finetuning Large-Scale Pre-trained Language Models for Conversational Recommendation with Knowledge Graph

In this paper, we present a pre-trained language model (PLM) based frame...

Towards Unified Conversational Recommender Systems via Knowledge-Enhanced Prompt Learning

Conversational recommender systems (CRS) aim to proactively elicit user ...

COOKIE: A Dataset for Conversational Recommendation over Knowledge Graphs in E-commerce

In this work, we present a new dataset for conversational recommendation...

A scale invariant ranking function for learning-to-rank: a real-world use case

Nowadays, Online Travel Agencies provide the main service for booking ho...

Knowledge Graph-enhanced Sampling for Conversational Recommender System

The traditional recommendation systems mainly use offline user data to t...

Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusion

Conversational recommender systems (CRS) aim to recommend high-quality i...

User Memory Reasoning for Conversational Recommendation

We study a conversational recommendation model which dynamically manages...
This week in AI

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