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.