@article{Wang2026, 
author = {Jiawei Wang and Sai Gu and Xiaotong Zhou and Ren Li and Yuxiang Zheng and Debin Liu and Ruonan Zhao and Huazhong Liu and M. Jamal Deen},
title = {HOSVD++: A Tensor-Based High Order SVD++ Recommendation System},
year = {2026},
journal = {Big Data Mining and Analytics},
volume = {9},
number = {3},
pages = {863-877},
keywords = {Recommendation Systems (RSs), Collaborative Filtering (CF), Singular Value Decomposition++ (SVD++), tensor decomposition, temporal dynamics, High-Order SVD++ (HO-SVD++)},
url = {https://www.sciopen.com/article/10.26599/BDMA.2025.9020060},
doi = {10.26599/BDMA.2025.9020060},
abstract = {In the context of big data enabling e-commerce, content platforms, and social networks, Recommendation Systems (RSs) play a crucial role in providing personalized items and services suggestions to users. Among the various RSs approaches, Collaborative Filtering (CF) approaches, particularly Singular Value Decomposition++ (SVD++) algorithm, have gained widespread adoption due to their ability to leverage both explicit and implicit feedback derived from user history interaction data. This data includes user-item status, purchase history, and user interaction. However, traditional matrix-based methods, such as SVD++, often struggle to capture the multi-dimensional features and temporal dynamics inherent in real-world user-item data. To address this limitation, we propose a novel tensor-based High-Order SVD++ (HO-SVD++) recommendation method. This approach employs tensors to model multi-feature interaction data, with a particular emphasis on temporal dynamics. Specifically, we introduce a novel method for segmenting recommendation data based on user rating periods and construct a tensor to encapsulate these temporal features. Additionally, we propose a user-item correlation CF method that extracts implicit feature relationships between users and items. Building on this, we present the HO-SVD++ method, which is specifically designed for recommendation tasks involving multiple latent factors. Furthermore, we introduce a comprehensive recommendation framework based on the HO-SVD++ method. Experimental results on three classical recommendation datasets demonstrate that the proposed HO-SVD++ algorithm outperforms several classical and neural recommendation baselines in terms of recommendation accuracy.}
}