现代电子技术2026，Vol.49Issue(18)：73-76,84,5.DOI:10.16652/j.issn.1004-373X.2026.18.012
海量信息语义的适配推荐系统设计
Design of adaptive recommendation system for massive information semantic
摘要
Abstract
In order to understand the deep meaning of user queries,extract contextual semantic features,and improve the timeliness and scene adaptability of recommendation results,an adaptation recommendation system for massive information semantic is designed.In the interface module of the system,a massive data collection interface is used to collect and recommend massive information related to the target from different data sources.In the storage module,the Hadoop distributed file system is used to store and collect massive information.In the adaptation recommendation module,the convolutional neural network(CNN)and bidirectional long short-term memory network(BiLSTM)are used to extract local and contextual semantic features from stored massive information.The deep target semantic features can be obtained by weighting and fusing two semantic features,so as to accurately understand the deep meaning of user queries.The adaptation score of target recommendations is calculated based on user preference vectors.The targets with high adaptability scores are selected as adaptive recommendation results to improve the timeliness and scenario adaptability of recommendation results.The experimental results show that the designed system can effectively extract deep semantic features of the target and complete adaptive recommendations.Under different levels of information sparsity,the normalized loss cumulative gain of the system's adaptive recommendation is relatively high,indicating a high accuracy of the adaptive recommendation.关键词
海量信息/语义特征/适配推荐/卷积神经网络/长短期记忆网络/用户偏好Key words
massive information/semantic feature/adaptation recommendation/convolutional neural network/long short-term memory network/user preference分类
信息技术与安全科学引用本文复制引用
邵华,郭蕊翾,孔庆国,金淼..海量信息语义的适配推荐系统设计[J].现代电子技术,2026,49(18):73-76,84,5.
