# Dynamic Collaborative Filtering for Matrix- and Tensor-based Recommender Systems

arXiv: https://arxiv.org/abs/2312.10064 · HTML: https://mlsanity.com/paper/2312.10064/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 22 references that are in the map.
- First posted on arXiv 4 Dec 2023.

## Builds on (most cited first, 7 of 7)

- [Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/1808.09781/) (2018)
- [S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization](https://mlsanity.com/paper/2008.07873/) (2020)
- [Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation](https://mlsanity.com/paper/2110.05730/) (2021)
- [Tensor Methods and Recommender Systems](https://mlsanity.com/paper/1603.06038/) (2016)
- [Effective and Efficient Training for Sequential Recommendation using Recency Sampling](https://mlsanity.com/paper/2207.02643/) (2022)
- [Tensor-based Sequential Learning via Hankel Matrix Representation for Next Item Recommendations](https://mlsanity.com/paper/2212.05720/) (2022)
- [Dynamic Modeling of User Preferences for Stable Recommendations](https://mlsanity.com/paper/2104.05047/) (2021)

## Abstract (arXiv)

In production applications of recommender systems, a continuous data flow is employed to update models in real-time. Many recommender models often require complete retraining to adapt to new data. In this work, we introduce a novel collaborative filtering model for sequential problems known as Tucker Integrator Recommender - TIRecA. TIRecA efficiently updates its parameters using only the new data segment, allowing incremental addition of new users and items to the recommender system. To demonstrate the effectiveness of the proposed model, we conducted experiments on four publicly available datasets: MovieLens 20M, Amazon Beauty, Amazon Toys and Games, and Steam. Our comparison with general matrix and tensor-based baselines in terms of prediction quality and computational time reveals that TIRecA achieves comparable quality to the baseline methods, while being 10-20 times faster in training time.