# The Continuous Cold Start Problem in e-Commerce Recommender Systems

arXiv: https://arxiv.org/abs/1508.01177 · HTML: https://mlsanity.com/paper/1508.01177/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Aug 2015.

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

- [Privacy Preserving Inference of Personalized Content for Out of Matrix Users](https://mlsanity.com/paper/2508.14905/) (2025)

## Abstract (arXiv)

Many e-commerce websites use recommender systems to recommend items to users. When a user or item is new, the system may fail because not enough information is available on this user or item. Various solutions to this `cold-start problem' have been proposed in the literature. However, many real-life e-commerce applications suffer from an aggravated, recurring version of cold-start even for known users or items, since many users visit the website rarely, change their interests over time, or exhibit different personas. This paper exposes the `Continuous Cold Start' (CoCoS) problem and its consequences for content- and context-based recommendation from the viewpoint of typical e-commerce applications, illustrated with examples from a major travel recommendation website, Booking.com.