@inproceedings{
liu2024deeply,
title={Deeply Fusing Semantics and Interactions for Item Representation Learning via Topology-driven Pre-training},
author={Shiqin Liu and Chaozhuo Li and Xi Zhang and Minjun Zhao and yuanbo xu and Jiajun Bu},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=JUZXOKPhXd}
}
Learning item representation is crucial for a myriad of on-line e-commerce applications. 
The nucleus of retail item representation learning is how to properly fuse the semantics within a single item, and the interactions across different items generated by user behaviors (e.g., co-click or co-view). 
Product semantics depict the intrinsic characteristics of the item, while the interactions describe the relationships between items from the perspective of human perception. 
Existing approaches either solely rely on a single type of information or loosely couple them together, leading to hindered representations. 
In this work, we propose a novel model named TESPA to reinforce semantic modeling and interaction modeling mutually. 
Specifically, collaborative filtering signals in the interaction graph are encoded into the language models through fine-grained topological pre-training, and the interaction graph is further enriched based on semantic similarities. 
After that, a novel multi-channel co-training paradigm is proposed to deeply fuse the semantics and interactions under a unified framework. 
In a nutshell, TESPA is capable of enjoying the merits of both sides to facilitate item representation learning. 
Experimental results of on-line and off-line evaluations demonstrate the superiority of our proposal.
Deeply Fusing Semantics and Interactions for Item Representation Learning via Topology-driven Pre-training
Shiqin Liu Chaozhuo Li Xi Zhang Minjun Zhao yuanbo xu Jiajun Bu