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An autoencoder-based recommendation framework toward cold start problem

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Abstract

Recommender methods have been effectively used in both academic and industrial settings. However, the cold start problem with scarce prior information has become the barrier hindering recommender systems from gaining further improvements. To overcome this issue, this article proposes a novel autoencoder framework referred to as CSRec, which owns the merits of both neural networks and collaborative filtering. Specifically, to search the nearest neighbors for cold start items, CSRec learns item representation and carries out clustering for items via k-means++ method. After that, with the nearest item cluster, CSRec could perform rating prediction for the cold start items through the autoencoder architecture, which could reconstruct the input space directly. Identically, CSRec could also perform cold start recommendations for users through the neural network. In practice, with the autoencoder architecture, CSRec owns powerful capability in computation and representation, which could deeply exploit the inner relationship for items and yield high performance in addressing cold start issues. Moreover, it could enhance the novelty and diversity of cold start recommendations. Experiments on CiaoDVDs and DoubanMovie certificate the superiority of CSRec in addressing the cold start issue, which could yield accurate performance in terms of RMSE, MAE and top-K and outperform other benchmark recommender approaches significantly.

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Data availability

No datasets were generated or analyzed during the current study.

Notes

  1. https://www.dbpedia.org/

  2. http://www.public.asu.edu/-jtang20/datasetcode/truststudy.htm.

  3. http://www.douban.com.

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Funding

This work is partially supported by Intelligent Policing Key Laboratory of Sichuan Province (No. ZNJW2024KFQN008), Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies (2022B1212010005)

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Authors and Affiliations

  1. School of Computer and Software Engineering, Xihua University, Chengdu, People’s Republic of China

    Wang Zhou

  2. Intelligent Policing Key Laboratory of Sichuan Province, Sichuan Police College, Luzhou, People’s Republic of China

    Ying Tian

  3. School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, People’s Republic of China

    Amin Ul Haq

  4. Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Alkharj, Saudi Arabia

    Sultan Ahmad

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  1. Wang Zhou
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  2. Ying Tian
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  3. Amin Ul Haq
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  4. Sultan Ahmad
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Contributions

Wang Zhou contributed to Conceptualization, Data curation, Software, Project Administration, and Writing—original draft. Ying Tian: Data curation, Writing, Project administration, Formal analysis, Writing—review and editing, and Investigation. Amin Ul Haq contributed to Methodology, Conceptualization, and Formal analysis. Sultan Ahmad contributed to Formal analysis, Project Administration, and Writing—review and editing.

Corresponding author

Correspondence to Ying Tian.

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Zhou, W., Tian, Y., Haq, A.U. et al. An autoencoder-based recommendation framework toward cold start problem. J Supercomput 81, 234 (2025). https://doi.org/10.1007/s11227-024-06721-6

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