微型电脑应用2025，Vol.41Issue(2)：93-97,5.
基于SBERT模型的神经协同过滤
Neural Collaborative Filtering Based on SBERT Model
摘要
Abstract
To address the problem of insufficient utilization of auxiliary information in the traditional neural collaborative filte-ring model,this study proposes a neural collaborative filtering model combined with a natural language processing method.The proposed method uses review data as user features and embeds them into the neural collaborative filtering model,thus compen-sating for the lack of recommendation accuracy and personalization caused by the lack of auxiliary features in this model.The SBERT model is used to semantically extract the user review text,and the user auxiliary features are obtained through cluste-ring,and then combined with the neural collaborative filtering model through the embedding layer.In the neural collaborative filtering framework,the features obtained by learning from both linear and nonlinear aspects,simulate the interaction between users and items,and combine them in the final hidden layer to give predicted scores.The combined model proposed in this study is experimentally compared with other similar recommendation models under the same data set.The experimental results show that the combined model can effectively improve the recommendation prediction compared with other similar recommenda-tion models.关键词
特征提取/自然语言处理/神经协同过滤/推荐系统Key words
feature extraction/natural language processing/neural collaborative filtering/recommendation system分类
信息技术与安全科学引用本文复制引用
邵必林,刘铮,孙皓雨..基于SBERT模型的神经协同过滤[J].微型电脑应用,2025,41(2):93-97,5.基金项目
智能新零售系类软件系统及相关前沿技术研究(20200153) （20200153）
