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Found 22 papers, 2 papers with code
Bayesian Off-Policy Evaluation and Learning for Large Action Spaces
In this framework, we propose sDM, a generic Bayesian approach designed for OPE and OPL, grounded in both algorithmic and theoretical foundations.
Position Paper: Why the Shooting in the Dark Method Dominates Recommender Systems Practice; A Call to Abandon Anti-Utopian Thinking
Applied recommender systems research is in a curious position.
Fast Slate Policy Optimization: Going Beyond Plackett-Luce
We compare our method to the commonly adopted Plackett-Luce policy class and demonstrate the effectiveness of our approach on problems with action space sizes in the order of millions.
Exponential Smoothing for Off-Policy Learning
In particular, it is also valid for standard IPS without making the assumption that the importance weights are bounded.
Learning from aggregated data with a maximum entropy model
Aggregating a dataset, then injecting some noise, is a simple and common way to release differentially private data. However, aggregated data -- even without noise -- is not an appropriate input for machine learning classifiers. In this work, we show how a new model, similar to a logistic regression, may be learned from aggregated data only by approximating the unobserved feature distribution with a maximum entropy hypothesis.
Offline Evaluation of Reward-Optimizing Recommender Systems: The Case of Simulation
Naturally, the reason for this is that we can directly measure utility metrics that rely on interventions, being the recommendations that are being shown to users.
Probabilistic Rank and Reward: A Scalable Model for Slate Recommendation
We introduce Probabilistic Rank and Reward (PRR), a scalable probabilistic model for personalized slate recommendation.
Fast Offline Policy Optimization for Large Scale Recommendation
Personalised interactive systems such as recommender systems require selecting relevant items from massive catalogs dependent on context.
Causal Inference, is just Inference: A beautifully simple idea that not everyone accepts
It is often argued that causal inference is a step that follows probabilistic estimation in a two step procedure, with a separate statistical estimation and causal inference step and each step is governed by its own principles.
Combining Reward and Rank Signals for Slate Recommendation
In this paper, we formulate several Bayesian models that incorporate the reward signal (Reward model), the rank signal (Rank model), or both (Full model), for non-personalized slate recommendation.
From Clicks to Conversions: Recommendation for long-term reward
Recommender systems are often optimised for short-term reward: a recommendation is considered successful if a reward (e. g. a click) can be observed immediately after the recommendation.
BLOB : A Probabilistic Model for Recommendation that Combines Organic and Bandit Signals
In contrast, the organic signal is typically strong and covers most items, but is not always relevant to the recommendation task.
Reconsidering Analytical Variational Bounds for Output Layers of Deep Networks
The combination of the re-parameterization trick with the use of variational auto-encoders has caused a sensation in Bayesian deep learning, allowing the training of realistic generative models of images and has considerably increased our ability to use scalable latent variable models.
Learning from Bandit Feedback: An Overview of the State-of-the-art
In order to handle this "bandit-feedback" setting, several Counterfactual Risk Minimisation (CRM) methods have been proposed in recent years, that attempt to estimate the performance of different policies on historical data.
Recommendation System-based Upper Confidence Bound for Online Advertising
The proposed method has been targeted to the problem of the product recommendation in the online advertising.
On the Value of Bandit Feedback for Offline Recommender System Evaluation
The procedure aims to give an answer to the question: "Given the natural sequence of user-item interactions up to time t, can we predict which item the user will interact with at time t+1?".
A Bayesian Solution to the M-Bias Problem
It is common practice in using regression type models for inferring causal effects, that inferring the correct causal relationship requires extra covariates are included or ``adjusted for''.
Latent Variable Session-Based Recommendation
An attractive feature of the latent variable approach is that, as the user continues to act, the posterior on the user's state tightens reflecting the recommender system's increased knowledge about that user.
Three Methods for Training on Bandit Feedback
There are three quite distinct ways to train a machine learning model on recommender system logs.
RecoGym: A Reinforcement Learning Environment for the problem of Product Recommendation in Online Advertising
Recommender Systems are becoming ubiquitous in many settings and take many forms, from product recommendation in e-commerce stores, to query suggestions in search engines, to friend recommendation in social networks.
