计算机应用研究2026，Vol.43Issue(2)：443-451,9.DOI:10.19734/j.issn.1001-3695.2025.06.0180
基于局部敏感哈希聚类的双通道自编码器-协同过滤混合推荐算法
Hybrid recommendation algorithm based on LSH clustering and dual-channel AutoEncoder-collaborative filtering
摘要
Abstract
To address the data-sparsity and cold-start problems in recommender systems,this paper proposed a dual-channel AutoEncoder-collaborative filtering hybrid algorithm based on locality-sensitive hashing(LSH)clustering.Firstly,it exploited LSH to cluster users efficiently,cutting computational overhead.Next,it designed a dual-channel AutoEncoder to jointly learn deep representations of two heterogeneous information sources—user rating behavior and movie genre preferences.Finally,a hybrid collaborative-filtering mechanism refined the predicted scores.Experiments on MovieLens-100K,MovieLens-1 M,and Amazon-Book show that the proposed model achieves NDCG@5 values of 0.371 4,0.568 9,and 0.737,respectively—up to 16％higher than the best existing methods.RMSE for rating prediction drops to 0.326 6,0.443 5,and 0.560 6,an im-provement of over 69.6％compared with the AutoRec baseline.The model outperforms competitors in recommendation accu-racy,ranking quality,and coverage,demonstrating its scalability and robustness in large-scale,highly sparse scenarios.关键词
双通道自编码器/协同过滤/聚类增强/混合推荐系统Key words
dual-channel AutoEncoder/collaborative filtering/cluster-enhanced/hybrid recommendation system分类
信息技术与安全科学引用本文复制引用
梅少伟,张圣筛..基于局部敏感哈希聚类的双通道自编码器-协同过滤混合推荐算法[J].计算机应用研究,2026,43(2):443-451,9.基金项目
上海杉达学院校级基金资助项目(2024YB09) （2024YB09）
2025年国家大学生创新创业资助项目(202511833013) （202511833013）
