Contextual Bandit

Primary authors: Vijay Yadav, Deepak HR Co-author : Narasimha 1. INTRODUCTION MakeMyTrip has multiple ranking/recommendation systems. We use learning to rank, sequence-based recommendation models, content or behavior representation-based algorithms, and collaborative filtering algorithms too. After the customer browses around, searching for hotels in city A, B or C, a substantial portion search…

October 12th, 2020 In the article “Carousel Personalization in Music Streaming Apps with Contextual Bandits." Bendada, W., Salha, G., & Bontempelli, T. (2020). the authors present a model for carousel personalization as a contextual multi-armed bandit problem. …

Coauthored with Shubha Shedthikere and Shyam Sunder 1. Introduction Swiggy Ads platform supports ads of restaurant partners and various FMCG/non-FMCG brand partners across its different business verticals (Food, Instamart, Meat). With millions of customers interacting with the app each day pan-India, it provides an opportunity for our ad partners to reach large-scale…

Authors: Qiang Chen, Jiayuan Dong, Tal Levy, and Jaya Kawale Tubi is an advertiser-based video on demand (AVOD) streaming company that allows viewers to watch any content available on the service for free. We have an extensive content library with various content, including movies, TV series, live news, and sports…

Cowriter — Narasimha.M In previous MakeMyTrip blogs we discussed decision-making systems in e-commerce space recommending actions based on a rule or a learning system. We discussed how reinforcement learning methods, contextual bandits plus deep learning, and other function approximators can be used to learn expected reward. In those conversations authors…

TLDR We explore how Netflix uses machine learning to customize each subscriber’s homepages to display the most relevant content. Outline Intro What is a recommendation engine? How Netflix uses machine learning Netflix recommendation engine Customizing user interface Improve video quality Closing Intro Netflix has radically evolved TV and film engagement through their streaming…

Co-authored by data scientist RaviKiran Daparthy Other key contributors (data scientists): Chandrakant N, Tarang Agrawal 1. Introduction Many e-commerce sites or apps have subsystems or microservices running behind the scenes, making many decisions. …

This article introduces the contextual bandits problem and an efficient algorithm based on a youtube talk and the paper “Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits”. Feel free to check them out directly for a more detailed explanation. Problem Abstract The general idea of contextual bandits is to…

Simulate a content personalization scenario with Vowpal Wabbit using contextual bandits to make choices between actions in a given context. INTRODUCTION Recommending relevant and personalised content to users is crucial for media services providers, e-commerce platforms, content based platforms etc. Indeed, effective recommender systems improve the users’ experience and engagement on…