# Adaptive, Personalized Diversity for Visual Discovery

arXiv: https://arxiv.org/abs/1810.01477 · HTML: https://mlsanity.com/paper/1810.01477/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Oct 2018.

## Built on by (most cited first, 8 of 8)

- [Sliding Spectrum Decomposition for Diversified Recommendation](https://mlsanity.com/paper/2107.05204/) (2021)
- [ResAct: Reinforcing Long-term Engagement in Sequential Recommendation with Residual Actor](https://mlsanity.com/paper/2206.02620/) (2022)
- [An Experimental Design Approach for Regret Minimization in Logistic Bandits](https://mlsanity.com/paper/2202.02407/) (2022)
- [Efficient Optimal Selection for Composited Advertising Creatives with Tree Structure](https://mlsanity.com/paper/2103.01453/) (2021)
- [Maximizing Submodular Functions for Recommendation in the Presence of Biases](https://mlsanity.com/paper/2305.02806/) (2023)
- [Self-supervised Multi-view Disentanglement for Expansion of Visual Collections](https://mlsanity.com/paper/2302.02249/) (2023)
- [Position bias in features](https://mlsanity.com/paper/2402.02626/) (2024)
- [CAViaR: Context Aware Video Recommendations](https://mlsanity.com/paper/2304.08435/) (2023)

## Abstract (arXiv)

Search queries are appropriate when users have explicit intent, but they perform poorly when the intent is difficult to express or if the user is simply looking to be inspired. Visual browsing systems allow e-commerce platforms to address these scenarios while offering the user an engaging shopping experience. Here we explore extensions in the direction of adaptive personalization and item diversification within Stream, a new form of visual browsing and discovery by Amazon. Our system presents the user with a diverse set of interesting items while adapting to user interactions. Our solution consists of three components (1) a Bayesian regression model for scoring the relevance of items while leveraging uncertainty, (2) a submodular diversification framework that re-ranks the top scoring items based on category, and (3) personalized category preferences learned from the user's behavior. When tested on live traffic, our algorithms show a strong lift in click-through-rate and session duration.