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App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 1

Hybrid recommendation algorithm based on LSH clustering and dual-channel AutoEncoder-collaborative filtering

Mei Shaowei1
Zhang Shengshai2,3
1. School of Computer & Information Engineering, Shanghai Second Polytechnic University, Shanghai 201209, China
2. Business School, University of Shanghai Science & Technology, Shanghai 200093, China
3. School of Information Science & Technology, Sanda University, Shanghai 201209, China
DOI: 10.19734/j.issn.1001-3695.2025.06.0180

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. First, LSH is exploited to cluster users efficiently, cutting computational overhead. Next, a dual-channel autoencoder is designed to jointly learn deep representations of two heterogeneous information sources—user rating behavior and movie genre preferences. Finally, a hybrid collaborative-filtering mechanism refines the predicted scores. Experiments on MovieLens-100K, MovieLens-1M, and Amazon-Book show that the proposed model achieves NDCG@5 values of 0.3714, 0.5689, and 0.737, respectively—up to 13.8 % higher than the best existing methods. RMSE for rating prediction drops to 0.3266, 0.4435, and 0.5606, an improvement of over 70 % compared with the AutoRec baseline. The model outperforms competitors in recommendation accuracy, ranking quality, and coverage, demonstrating its scalability and robustness in large-scale, highly sparse scenarios.

Foundation Support

上海杉达学院校级基金资助项目(2024YB09)
2025年国家大学生创新创业资助项目(202511833013)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.06.0180
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 1

Publish History

[2025-09-13] Accepted Paper

Cite This Article

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 (1). (2025-09-17). https://doi.org/10.19734/j.issn.1001-3695.2025.06.0180. (Mei Shaowei, Zhang Shengshai. Hybrid recommendation algorithm based on LSH clustering and dual-channel AutoEncoder-collaborative filtering [J]. Application Research of Computers, 2026, 43 (1). (2025-09-17). https://doi.org/10.19734/j.issn.1001-3695.2025.06.0180. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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