# Reweighting Clicks with Dwell Time in Recommendation

arXiv: https://arxiv.org/abs/2209.09000 · HTML: https://mlsanity.com/paper/2209.09000/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2022.

## Built on by (most cited first, 6 of 6)

- [CWRCzech: 100M Query-Document Czech Click Dataset and Its Application to Web Relevance Ranking](https://mlsanity.com/paper/2405.20994/) (2024)
- [Time Matters: Enhancing Pre-trained News Recommendation Models with Robust User Dwell Time Injection](https://mlsanity.com/paper/2405.12486/) (2024)
- [Interpretable Triplet Importance for Personalized Ranking](https://mlsanity.com/paper/2407.19469/) (2024)
- [Hierarchical Exponential-Gaussian Mixtures for Watch-Time Distribution Prediction](https://mlsanity.com/paper/2608.23356/) (2026)
- [Breaker: Removing Shortcut Cues with User Clustering for Single-slot Recommendation System](https://mlsanity.com/paper/2506.00828/) (2025)
- [Improved Personalized Headline Generation via Denoising Fake Interests from Implicit Feedback](https://mlsanity.com/paper/2508.07178/) (2025)

## Abstract (arXiv)

The click behavior is the most widely-used user positive feedback in recommendation. However, simply considering each click equally in training may suffer from clickbaits and title-content mismatching, and thus fail to precisely capture users' real satisfaction on items. Dwell time could be viewed as a high-quality quantitative indicator of user preferences on each click, while existing recommendation models do not fully explore the modeling of dwell time. In this work, we focus on reweighting clicks with dwell time in recommendation. Precisely, we first define a new behavior named valid read, which helps to select high-quality click instances for different users and items via dwell time. Next, we propose a normalized dwell time function to reweight click signals in training for recommendation. The Click reweighting model achieves significant improvements on both offline and online evaluations in real-world systems.