# Dynamic Tensor Recommender Systems

arXiv: https://arxiv.org/abs/2003.05568 · HTML: https://mlsanity.com/paper/2003.05568/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Mar 2020.

## Built on by (most cited first, 12 of 12)

- [Nonparametric Factor Trajectory Learning for Dynamic Tensor Decomposition](https://mlsanity.com/paper/2207.02446/) (2022)
- [Streaming Factor Trajectory Learning for Temporal Tensor Decomposition](https://mlsanity.com/paper/2310.17021/) (2023)
- [Dynamic Tensor Decomposition via Neural Diffusion-Reaction Processes](https://mlsanity.com/paper/2310.19666/) (2023)
- [Factorized Structured Regression for Large-Scale Varying Coefficient Models](https://mlsanity.com/paper/2205.13080/) (2022)
- [Functional Complexity-adaptive Temporal Tensor Decomposition](https://mlsanity.com/paper/2502.06164/) (2025)
- [Additive Higher-Order Factorization Machines](https://mlsanity.com/paper/2205.14515/) (2022)
- [A Survey of Recommender System Techniques and the Ecommerce Domain](https://mlsanity.com/paper/2208.07399/) (2022)
- [Factorized Fusion Shrinkage for Dynamic Relational Data](https://mlsanity.com/paper/2210.00091/) (2022)
- [Alteration Detection of Tensor Dependence Structure via Sparsity-Exploited Reranking Algorithm](https://mlsanity.com/paper/2310.08798/) (2023)
- [An Extended Kalman Filter Integrated Latent Feature Model on Dynamic Weighted Directed Graphs](https://mlsanity.com/paper/2407.21376/) (2024)
- [Score-Based Model for Low-Rank Tensor Recovery](https://mlsanity.com/paper/2506.22295/) (2025)
- [Recommending Composite Items Using Multi-Level Preference Information: A Joint Interaction Modeling Approach](https://mlsanity.com/paper/2601.19005/) (2026)

## Abstract (arXiv)

Recommender systems have been extensively used by the entertainment industry, business marketing and the biomedical industry. In addition to its capacity of providing preference-based recommendations as an unsupervised learning methodology, it has been also proven useful in sales forecasting, product introduction and other production related businesses. Since some consumers and companies need a recommendation or prediction for future budget, labor and supply chain coordination, dynamic recommender systems for precise forecasting have become extremely necessary. In this article, we propose a new recommendation method, namely the dynamic tensor recommender system (DTRS), which aims particularly at forecasting future recommendation. The proposed method utilizes a tensor-valued function of time to integrate time and contextual information, and creates a time-varying coefficient model for temporal tensor factorization through a polynomial spline approximation. Major advantages of the proposed method include competitive future recommendation predictions and effective prediction interval estimations. In theory, we establish the convergence rate of the proposed tensor factorization and asymptotic normality of the spline coefficient estimator. The proposed method is applied to simulations and IRI marketing data. Numerical studies demonstrate that the proposed method outperforms existing methods in terms of future time forecasting.