It has been long known that quantum computing the potential to revolutionize way we find solutions of problems are difficult solve on classical computers. was only recently small but functional computers have become available cloud, allowing test their potential. In this paper propose leverage capabilities address an important task for recommender systems providers, optimal selection recommendation carousels. many video-on-demand and music streaming services user is provided with a homepage...

In this paper we provide a description of the methods used as team BanaNeverAlone for ACM RecSys Challenge 2020, organized by Twitter. The challenge addresses problem user engagement prediction: goal is to predict probability (Like, Reply, Retweet or with comment), based on series past interactions Twitter platform. Our proposed solution relies several features that extracted from original dataset, well consolidated models, such gradient boosting decision trees and neural networks. ensemble...

Data is a precious resource in today's society, and generated at an unprecedented constantly growing pace. The need to store, analyze, make data promptly available multitude of users introduces formidable challenges modern software platforms. These radically transformed all research fields that gravitate around management processing, with the introduction distributed data-intensive systems offer new programming models implementation strategies handle characteristics such as its volume, rate...

Many video-on-demand and music streaming services provide the user with a page consisting of several recommendation lists, i.e., widgets or swipeable carousels , each built specific criteria (e.g., most recent, TV series, etc.). Finding efficient strategies to select which display is an active research topic great industrial interest. In this setting, overall quality recommendations new algorithm cannot be assessed by measuring solely its individual quality. Rather, it should evaluated in...

It is common for video-on-demand and music streaming services to adopt a user interface composed of several recommendation lists, i.e., widgets or swipeable carousels, each generated according specific criterion algorithm (e.g., most recent, top popular, recommended you, editors' choice, etc.). Selecting the appropriate combination carousel has significant impact on satisfaction. A crucial aspect this that measure relevance new it not sufficient account solely its individual quality....

We investigate the role of uncertainty in decision-making problems with natural language as input. For such tasks, using Large Language Models agents has become norm. However, none recent approaches employ any additional phase for estimating agent about world during task. focus on a fundamental framework input, which is one contextual bandits, where context information consists text. As representative no estimation, we consider an LLM bandit greedy policy, picks action corresponding to...

The Off-Policy Evaluation (OPE) problem consists of evaluating the performance counterfactual policies with data collected by another one. This is utmost importance for various application domains, e.g., recommendation systems, medical treatments, and many others. To solve OPE problem, we resort to estimators, which aim estimate in most accurate way possible that would have had if they were deployed place logging policy. In literature, several estimators been developed, all different...

Many video-on-demand and music streaming services provide the user with a page consisting of several recommendation lists, i.e., widgets or swipeable carousels, each built specific criterion (e.g., most recent, TV series, etc.). Finding efficient strategies to select which carousels display is an active research topic great industrial interest. In this setting, overall quality recommendations new algorithm cannot be assessed by measuring solely its individual quality. Rather, it should...

Evaluating recommendation systems is a task of utmost importance and very active research field. While online evaluation the most reliable procedure, it may also be too expensive to perform, if not unfeasible. Therefore, researchers practitioners resort offline evaluation. Offline much more efficient scalable, but traditional approaches suffer from high bias. This issue led increased popularity counterfactual techniques. These techniques are used for learning in recommender reduce bias have...