An Enhanced Latent Factor Recommendation Approach for Sparse Datasets of E-Commerce Platforms
In certain newly established or niche e-commerce platforms, user–item interactions are often exceedingly sparse due to limited user bases or specialized product lines, posing significant obstacles to accurate personalized recommendations. To address these challenges, this paper proposes an enhanced...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
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| Series: | Systems |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-8954/13/5/372 |
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| Summary: | In certain newly established or niche e-commerce platforms, user–item interactions are often exceedingly sparse due to limited user bases or specialized product lines, posing significant obstacles to accurate personalized recommendations. To address these challenges, this paper proposes an enhanced recommendation approach based on a latent factor model. By leveraging factorization to uncover the hidden features of users and items and incorporating both user behavioral data and item attribute information, a multi-dimensional latent semantic space is constructed to more effectively capture the underlying relationships between user preferences and item properties. The method involves data preprocessing, model construction, user and item vectorization, and semantic-similarity-based recommendation generation. For empirical validation, we employ a real-world dataset gathered from an e-commerce platform, comprising 4645 ratings from 3445 users across 277 items in nine distinct categories. Experimental results demonstrate that, compared with conventional collaborative filtering methods, this approach achieves superior precision and recall even in highly sparse settings, showing stronger resilience under low-density conditions. These findings offer objective and feasible insights for advancing personalized recommendation techniques in newly established or niche e-commerce platforms. |
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| ISSN: | 2079-8954 |