基于亿级知识图谱预训练的会话式推荐系统改进
Improving Conversational Recommendation System by Pretraining on Billions Scale of Knowledge Graph
摘要：
电子商务平台中的会话推荐系统（CRS）旨在通过多个会话交互向用户推荐物品。点击率（CTR）预测模型通常用于对候选项进行排名。然而，大多数CRS都受到数据稀缺和稀疏的问题。为了解决这个问题，我们提出了一种新的知识增强深度交叉网络（K-DCN），这是一种两步（预训练和微调）CTR预测模型来推荐项目。首先根据用户、项目和会话信息构建十亿级会话知识图谱（CKG），然后引入知识图谱嵌入方法和图卷积网络分别对CKG进行语义和结构信息编码，使CTR预测模型能够感知用户的当前状态以及会话与项目之间的关系，本文基于预先训练好的CKG，引入用户状态和对话交互表示，提出K-DCN，通过深度交叉网络融合用户状态表示、对话交互表示和其他常规特征表示，实验结果表明，该方法的推荐效果明显优于基线，并在Alime中得到了实际应用。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
