Coarse-to-fine lightweight meta-embedding for ID-based recommendations  

作　　者：Yang WANG Haipeng LIU Zeqian YI Biao QIAN Meng WANG 

机构地区：[1]School of Computer Science and Information Engineering,Hefei University of Technology,Hefei 230009,China [2]College of Information and Intelligence,Hunan Agricultural University,Changsha 410125,China

出　　处：《Science China(Information Sciences)》2025年第4期78-93,共16页中国科学(信息科学)(英文版)

基　　金：supported in part by Research Projects of National Natural Science Foundation of China(Grant Nos.U21A20470,62172136,72188101);Institute of Advanced Medicine and Frontier Technology(Grant No.2023IHM01080)。

摘　　要：State-of-the-art recommender systems are increasingly focused on optimizing implementation efficiency,such as enabling on-device recommendations under memory constraints.Current methods commonly use lightweight embeddings for users and items or employ compact embeddings to enhance reusability and reduce memory usage.However,these approaches consider only the coarse-grained aspects of embeddings,overlooking subtle semantic nuances.This limitation results in an adversarial degradation of meta-embedding performance,impeding the system's ability to capture intricate relationships between users and items,leading to suboptimal recommendations.To address this,we propose a novel approach to efficiently learn meta-embeddings with varying grained and apply fine-grained meta-embeddings to strengthen the representation of their coarse-grained counterparts.Specifically,we introduce a recommender system based on a graph neural network,where each user and item is represented as a node.These nodes are directly connected to coarse-grained virtual nodes and indirectly linked to fine-grained virtual nodes,facilitating learning of multi-grained semantics.Fine-grained semantics are captured through sparse meta-embeddings,which dynamically balance embedding uniqueness and memory constraints.To ensure their sparseness,we rely on initialization methods such as sparse principal component analysis combined with a soft thresholding activation function.Moreover,we propose a weight-bridging update strategy that aligns coarse-grained meta-embedding with several fine-grained meta-embeddings based on the underlying semantic properties of users and items.Comprehensive experiments demonstrate that our method outperforms existing baselines.The code of our proposal is available at https://github.com/htyjers/C2F-MetaEmbed.

关 键 词：lightweight meta-embedding coarse-to-fine learning ID-based recommendations 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]