How can the gap between academic research and industrial practice in recommendation systems be bridged?

Apr 26, 2016 ... Machine Learning, Recommendation Systems, and Data Analysis at Cloud Academy ... In today's guest post, Alex Casalboni and Giacomo Marinangeli of ... Nov 18, 2020 ... The paper presents the development and the comparison of multiple recommendation systems, capable of making item suggestions, based on user, ... Deep learning has profoundly impacted many areas of machine learning. However, it took a while for its impact to be felt in the field of recommender systems. In ... There are great interests as well as many challenges in applying reinforcement learning (RL) to recommendation systems. In this setting, an online user is ... This document outlines the reference architecture for two functional machine Learning (ML) retail use cases. It was developed on VMware Cloud Foundation 4.0 ... Mar 29, 2022 ... Tutorial: Reinforcement learning for recommender systems ... Hands-on tutorial on RL and RLlib for building recommendation systems. In this 2-hour ... Content-based Recommendation Systems (CbRS) is a research area in which Machine Learning (ML) strategies can be applied with success. Moreover, recommender systems are among the most powerful machine learning systems that online retailers implement in order to drive incremental revenue. Mar 15, 2023 ... This survey aims to provide a timely and comprehensive overview of recent trends of deep reinforcement learning in recommender systems. Content-based filtering recommends items based on the similarity in features between an item and a user preference.