# Incremental Factorization Machines for Persistently Cold-starting Online Item Recommendation

arXiv: https://arxiv.org/abs/1607.02858 · HTML: https://mlsanity.com/paper/1607.02858/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jul 2016.

## Built on by (most cited first, 1 of 1)

- [MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models](https://mlsanity.com/paper/2509.07319/) (2025)

## Abstract (arXiv)

Real-world item recommenders commonly suffer from a persistent cold-start problem which is caused by dynamically changing users and items. In order to overcome the problem, several context-aware recommendation techniques have been recently proposed. In terms of both feasibility and performance, factorization machine (FM) is one of the most promising methods as generalization of the conventional matrix factorization techniques. However, since online algorithms are suitable for dynamic data, the static FMs are still inadequate. Thus, this paper proposes incremental FMs (iFMs), a general online factorization framework, and specially extends iFMs into an online item recommender. The proposed framework can be a promising baseline for further development of the production recommender systems. Evaluation is done empirically both on synthetic and real-world unstable datasets.