Search Results for author: Chi-Man Wong

Found 4 papers, 0 papers with code

PKGM: A Pre-trained Knowledge Graph Model for E-commerce Application

no code implementations2 Mar 2022 Wen Zhang, Chi-Man Wong, Ganqinag Ye, Bo Wen, Hongting Zhou, Wei zhang, Huajun Chen

On the one hand, it could provide item knowledge services in a uniform way with service vectors for embedding-based and item-knowledge-related task models without accessing triple data.

Knowledge Graphs Sequential Recommendation

Sparse Bayesian Learning with Diagonal Quasi-Newton Method for Large Scale Classification

no code implementations17 Jul 2021 Jiahua Luo, Chi-Man Vong, Chi-Man Wong, Jie Du

Sparse Bayesian Learning (SBL) constructs an extremely sparse probabilistic model with very competitive generalization.

Billion-scale Pre-trained E-commerce Product Knowledge Graph Model

no code implementations2 May 2021 Wen Zhang, Chi-Man Wong, Ganqiang Ye, Bo Wen, Wei zhang, Huajun Chen

As a backbone for online shopping platforms, we built a billion-scale e-commerce product knowledge graph for various item knowledge services such as item recommendation.

Knowledge Graphs

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

no code implementations30 Apr 2021 Chi-Man Wong, Fan Feng, Wen Zhang, Chi-Man Vong, Hui Chen, Yichi Zhang, Peng He, Huan Chen, Kun Zhao, Huajun Chen

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.

Click-Through Rate Prediction Knowledge Graph Embedding +1

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