海南热带海洋学院学报2025，Vol.32Issue(2)：97-105,9.DOI:10.13307/j.issn.2096-3122.2025.02.10
基于自然语言相似度检索的移动电商产品数据推荐方法
Mobile E-commerce Product Data Recommendation Method Based on Natural Language Similarity Retrieval
摘要
Abstract
Due to the sparsity of mobile e-commerce product data and the frequent use of diverse expressions by us-ers to describe their needs and preferences,it is difficult to capture the deep-level information behind natural language,which results in a decrease in the accuracy of recommendations results.In order to recommend accurate mobile e-com-merce product data for users,a recommendation method for mobile e-commerce product data was proposed based on natu-ral language similarity retrieval.By integrating the interactive attention mechanism into twin networks,merging natural lan-guage matrices to form an attention matrix,and concatenating the new and old matrices,the deeper information in natu-ral language and the similarity calculation results of natural language were obtained through a Transformer encoder.Through the Boolean query mechanism to form the retrieval model,the natural language similarity retrieval was completed.The refined user clustering model was constructed through the binary K-means clustering algorithm to calculate the aver-age interest level of the user group,quantify the similarity between the mobile e-commerce product data and the user's needs,and form the final mobile e-commerce product data recommendation list based on the information of the nearest neighbors.The results showed that the proposed method significantly improves the coverage of mobile e-commerce product data,up to 97%,with strong robustness and larger throughput values,so it can recommend mobile e-commerce product data for the users to meet their needs.关键词
自然语言/相似度检索/移动电商产品/数据推荐/聚类算法Key words
natural language/similarity retrieval/mobile e-commerce products/data recommendation/clustering algo-rithm分类
信息技术与安全科学引用本文复制引用
任华新,景慎艳..基于自然语言相似度检索的移动电商产品数据推荐方法[J].海南热带海洋学院学报,2025,32(2):97-105,9.基金项目
辽宁省高等学校科学研究一般项目(LJKZ1328) （LJKZ1328）
