# A Scalable Recommendation Engine for New Users and Items

arXiv: https://arxiv.org/abs/2209.06128 · HTML: https://mlsanity.com/paper/2209.06128/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 128 references that are in the map.
- First posted on arXiv 6 Sep 2022.

## Builds on (most cited first, 23 of 23)

- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020)
- [Generative Adversarial User Model for Reinforcement Learning Based Recommendation System](https://mlsanity.com/paper/1812.10613/) (2018)
- [Low-Rank Generalized Linear Bandit Problems](https://mlsanity.com/paper/2006.02948/) (2020)
- [On Context-Dependent Clustering of Bandits](https://mlsanity.com/paper/1608.03544/) (2016)
- [A Survey on Contextual Multi-armed Bandits](https://mlsanity.com/paper/1508.03326/) (2015)
- [Spectral Collaborative Filtering](https://mlsanity.com/paper/1808.10523/) (2018)
- [Stochastic Low-Rank Bandits](https://mlsanity.com/paper/1712.04644/) (2017)
- [Estimation Considerations in Contextual Bandits](https://mlsanity.com/paper/1711.07077/) (2017)
- [Solving Bernoulli Rank-One Bandits with Unimodal Thompson Sampling](https://mlsanity.com/paper/1912.03074/) (2019)
- [Matching while Learning](https://mlsanity.com/paper/1603.04549/) (2016)
- [Hybrid Recommender Systems: A Systematic Literature Review](https://mlsanity.com/paper/1901.03888/) (2019)
- [Multinomial Logit Bandit with Linear Utility Functions](https://mlsanity.com/paper/1805.02971/) (2018)
- [Hybrid Recommender System based on Autoencoders](https://mlsanity.com/paper/1606.07659/) (2016)
- [Bayesian Meta-Prior Learning Using Empirical Bayes](https://mlsanity.com/paper/2002.01129/) (2020)
- [Online Interactive Collaborative Filtering Using Multi-Armed Bandit with Dependent Arms](https://mlsanity.com/paper/1708.03058/) (2017)
- [Spoiled for Choice? Personalized Recommendation for Healthcare Decisions: A Multi-Armed Bandit Approach](https://mlsanity.com/paper/2009.06108/) (2020)
- [Cold-start recommendations in Collective Matrix Factorization](https://mlsanity.com/paper/1809.00366/) (2018)
- [Generalized Thompson Sampling for Contextual Bandits](https://mlsanity.com/paper/1310.7163/) (2013)
- [Auto-Encoding User Ratings via Knowledge Graphs in Recommendation Scenarios](https://mlsanity.com/paper/1706.07956/) (2017)
- [Beyond the Click-Through Rate: Web Link Selection with Multi-level Feedback](https://mlsanity.com/paper/1805.01702/) (2018)
- [Collaborative Generative Hashing for Marketing and Fast Cold-start Recommendation](https://mlsanity.com/paper/2011.00953/) (2020)
- [A Theoretical Analysis of Two-Stage Recommendation for Cold-Start Collaborative Filtering](https://mlsanity.com/paper/1601.04745/) (2016)
- [Scalable bundling via dense product embeddings](https://mlsanity.com/paper/2002.00100/) (2020)

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

- [Generative Augmented Inference of LLM-generated Data for Market Research: Theory and Empirical Evidence](https://mlsanity.com/paper/2604.14575/) (2026)

## Abstract (arXiv)

In many digital contexts such as online news and e-tailing with many new users and items, recommendation systems face several challenges: i) how to make initial recommendations to users with little or no response history (i.e., cold-start problem), ii) how to learn user preferences on items (test and learn), and iii) how to scale across many users and items with myriad demographics and attributes. While many recommendation systems accommodate aspects of these challenges, few if any address all. This paper introduces a Collaborative Filtering (CF) Multi-armed Bandit (B) with Attributes (A) recommendation system (CFB-A) to jointly accommodate all of these considerations. Empirical applications including an offline test on MovieLens data, synthetic data simulations, and an online grocery experiment indicate the CFB-A leads to substantial improvement on cumulative average rewards (e.g., total money or time spent, clicks, purchased quantities, average ratings, etc.) relative to the most powerful extant baseline methods.