Eliminating Information Cocoons: Product Diversification Recommendation Based on User Clustering and Hybrid Reranking
文献类型：期刊论文
| 作者 | Li, Jiaxin1,2; Fang, Jinyun1,2 |
| 刊名 | NEUROCOMPUTING
 |
| 出版日期 | 2026-03-14 |
| 卷号 | 670页码:15 |
| 关键词 | Diversified recommendation Information cocoons Local hybrid recommendation Hybrid reranking User clustering |
| ISSN号 | 0925-2312 |
| DOI | 10.1016/j.neucom.2025.132595 |
| 英文摘要 | Recommendation systems, while alleviating information overload, often over-specialize and trap users in "information cocoons." To address this, we propose a novel two-stage diversified recommendation framework that strategically separates accuracy optimization from diversity enhancement. In the first stage, we construct a clustering-based local-hybrid model (RHM). It fuses a global model, built on a genre-augmented rating matrix filled via the Weighted Slope One (WSO) algorithm, with local models derived from user clusters identified via Latent Dirichlet Allocation (LDA). This stage establishes a robust foundation of recommendation accuracy. In the second stage, we introduce a hybrid reranking strategy with an adaptive switching mechanism. For each user, it dynamically chooses between a threshold reranking method (which penalizes both item and genre popularity to boost genre coverage) and a greedy reranking method (which incorporates a binomial diversity framework to jointly optimize relevance and genre coverage). This stage is dedicated to diversity enhancement with minimal accuracy loss. Guided by the philosophy that breaking deep information filters requires dedicated, sequential optimization of accuracy and diversity, our framework offers a clear pathway toward more open recommendations. Experiments on a movie dataset show that RHM improves recommendation accuracy (nDCG) by 32.4 % over a standard UserCF baseline. The full diversified model (DRHM) further enhances genre coverage (GC) by 13.7 % and overall coverage (COV) by 56.1 %, while retaining 96.7 % of RHM's accuracy. The proposed framework effectively balances the accuracy-diversity trade-off, offering a practical pathway toward more open and equitable recommendation ecosystems. |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:001665483800002 |
| 出版者 | ELSEVIER |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/42867]  |
| 专题 | 中国科学院计算技术研究所 |
| 通讯作者 | Fang, Jinyun |
| 作者单位 | 1.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Jiaxin,Fang, Jinyun. Eliminating Information Cocoons: Product Diversification Recommendation Based on User Clustering and Hybrid Reranking[J]. NEUROCOMPUTING,2026,670:15. |
| APA | Li, Jiaxin,&Fang, Jinyun.(2026).Eliminating Information Cocoons: Product Diversification Recommendation Based on User Clustering and Hybrid Reranking.NEUROCOMPUTING,670,15. |
| MLA | Li, Jiaxin,et al."Eliminating Information Cocoons: Product Diversification Recommendation Based on User Clustering and Hybrid Reranking".NEUROCOMPUTING 670(2026):15. |
入库方式： OAI收割
来源：计算技术研究所
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