计算机与数字工程2025，Vol.53Issue(1)：158-163,6.DOI:10.3969/j.issn.1672-9722.2025.01.030
基于编码器增强的DeepFM推荐模型
DeepFM Recommendation Model Based on Encoder Enhancement
胡克富 1王卫东1
作者信息
- 1. 江苏科技大学计算机学院 镇江 212100
- 折叠
摘要
Abstract
Click-through rate(CTR)prediction is used to predict the probability of users clicking on recommended items,which is a key task for recommender systems and online advertising.Efficient feature interactions and interpretability of feature inter-actions are lacking in CTR prediction models.This paper proposes an EnDeepFM recommendation model with an encoder(Deep Neural Networks with Encoder Enhanced Factorization Machine,EnDeepFM),which encodes the embedded features through the Transformer encoder,and uses the bilinear function to generate different feature similarities of different feature pairs.Embeddings generated from the encoder facilitate further feature interactions.Finally,comparative experiments are conducted on the real datas-ets Criteo and MovieLens,and the experimental results show that the proposed algorithm has better predictive performance than the DeepFM model.关键词
CTR预测/编码器/深度学习/DeepFMKey words
CTR prediction/encoder/deep learning/DeepFM分类
信息技术与安全科学引用本文复制引用
胡克富,王卫东..基于编码器增强的DeepFM推荐模型[J].计算机与数字工程,2025,53(1):158-163,6.
