# BLOB : A Probabilistic Model for Recommendation that Combines Organic and Bandit Signals

arXiv: https://arxiv.org/abs/2008.12504 · HTML: https://mlsanity.com/paper/2008.12504/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Aug 2020.

## Built on by (most cited first, 19 of 19)

- [Advances and Challenges in Conversational Recommender Systems: A Survey](https://mlsanity.com/paper/2101.09459/) (2021)
- [PAC-Bayesian Offline Contextual Bandits With Guarantees](https://mlsanity.com/paper/2210.13132/) (2022)
- [Logarithmic Smoothing for Pessimistic Off-Policy Evaluation, Selection and Learning](https://mlsanity.com/paper/2405.14335/) (2024)
- [Exponential Smoothing for Off-Policy Learning](https://mlsanity.com/paper/2305.15877/) (2023)
- [Off-Policy Evaluation for Large Action Spaces via Policy Convolution](https://mlsanity.com/paper/2310.15433/) (2023)
- [On (Normalised) Discounted Cumulative Gain as an Off-Policy Evaluation Metric for Top-n Recommendation](https://mlsanity.com/paper/2307.15053/) (2023)
- [Optimal Baseline Corrections for Off-Policy Contextual Bandits](https://mlsanity.com/paper/2405.05736/) (2024)
- [On the Opportunities and Challenges of Offline Reinforcement Learning for Recommender Systems](https://mlsanity.com/paper/2308.11336/) (2023)
- [Multi-Objective Recommendation via Multivariate Policy Learning](https://mlsanity.com/paper/2405.02141/) (2024)
- [Probabilistic Rank and Reward: A Scalable Model for Slate Recommendation](https://mlsanity.com/paper/2208.06263/) (2022)
- [Bayesian Off-Policy Evaluation and Learning for Large Action Spaces](https://mlsanity.com/paper/2402.14664/) (2024)
- [Fast Offline Policy Optimization for Large Scale Recommendation](https://mlsanity.com/paper/2208.05327/) (2022)
- [Fast Slate Policy Optimization: Going Beyond Plackett-Luce](https://mlsanity.com/paper/2308.01566/) (2023)
- [Unified PAC-Bayesian Study of Pessimism for Offline Policy Learning with Regularized Importance Sampling](https://mlsanity.com/paper/2406.03434/) (2024)
- [Offline Evaluation of Reward-Optimizing Recommender Systems: The Case of Simulation](https://mlsanity.com/paper/2209.08642/) (2022)
- [Exploiting Similarities in A/B Testing with Off-Policy Estimation](https://mlsanity.com/paper/2506.10677/) (2025)
- [Residual Overfit Method of Exploration](https://mlsanity.com/paper/2110.02919/) (2021)
- [Sequential Off-Policy Learning with Logarithmic Smoothing](https://mlsanity.com/paper/2506.10664/) (2025)
- [Distributional Approximate Nearest Neighbour Search for Uncertainty-Aware Retrieval](https://mlsanity.com/paper/2606.04603/) (2026)

## Abstract (arXiv)

A common task for recommender systems is to build a pro le of the interests of a user from items in their browsing history and later to recommend items to the user from the same catalog. The users' behavior consists of two parts: the sequence of items that they viewed without intervention (the organic part) and the sequences of items recommended to them and their outcome (the bandit part). In this paper, we propose Bayesian Latent Organic Bandit model (BLOB), a probabilistic approach to combine the 'or-ganic' and 'bandit' signals in order to improve the estimation of recommendation quality. The bandit signal is valuable as it gives direct feedback of recommendation performance, but the signal quality is very uneven, as it is highly concentrated on the recommendations deemed optimal by the past version of the recom-mender system. In contrast, the organic signal is typically strong and covers most items, but is not always relevant to the recommendation task. In order to leverage the organic signal to e ciently learn the bandit signal in a Bayesian model we identify three fundamental types of distances, namely action-history, action-action and history-history distances. We implement a scalable approximation of the full model using variational auto-encoders and the local re-paramerization trick. We show using extensive simulation studies that our method out-performs or matches the value of both state-of-the-art organic-based recommendation algorithms, and of bandit-based methods (both value and policy-based) both in organic and bandit-rich environments.