Optimized Recommender Systems with Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2110.03039v1
• Date: Wed, 6 Oct 2021 19:54:55 GMT
• Title: Optimized Recommender Systems with Deep Reinforcement Learning
• Authors: Lucas Farris
• Abstract summary: This work investigates and develops means to setup a reproducible testbed, and evaluate different state of the art algorithms in a realistic environment. It entails a proposal, literature review, methodology, results, and comments.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recommender Systems have been the cornerstone of online retailers. Traditionally they were based on rules, relevance scores, ranking algorithms, and supervised learning algorithms, but now it is feasible to use reinforcement learning algorithms to generate meaningful recommendations. This work investigates and develops means to setup a reproducible testbed, and evaluate different state of the art algorithms in a realistic environment. It entails a proposal, literature review, methodology, results, and comments.

Related papers

• Pre-trained Language Model and Knowledge Distillation for Lightweight Sequential Recommendation [51.25461871988366]
  We propose a sequential recommendation algorithm based on a pre-trained language model and knowledge distillation. The proposed algorithm enhances recommendation accuracy and provide timely recommendation services.
  arXiv  Detail & Related papers  (2024-09-23T08:39:07Z)
• Predictive accuracy of recommender algorithms [0.0]
  A variety of algorithms for recommender systems have been developed and refined including applications of deep learning neural networks. Recent research reports point to a need to perform carefully controlled experiments to gain insights about the relative accuracy of different recommender algorithms. This investigation used publicly available sources of ratings data with a suite of three conventional recommender algorithms and two deep learning (DL) algorithms in controlled experiments to assess their comparative accuracy.
  arXiv  Detail & Related papers  (2024-06-26T19:25:07Z)
• Improvements on Recommender System based on Mathematical Principles [10.027420333081084]
  We will explain the Recommender System's algorithms based on mathematical principles, and find feasible methods for improvements. The algorithms based on probability have its significance in Recommender System, we will describe how they help to increase the accuracy and speed of the algorithms.
  arXiv  Detail & Related papers  (2023-04-26T14:13:46Z)
• Tree-Based Adaptive Model Learning [62.997667081978825]
  We extend the Kearns-Vazirani learning algorithm to handle systems that change over time. We present a new learning algorithm that can reuse and update previously learned behavior, implement it in the LearnLib library, and evaluate it on large examples.
  arXiv  Detail & Related papers  (2022-08-31T21:24:22Z)
• Emergent Instabilities in Algorithmic Feedback Loops [3.4711828357576855]
  We explore algorithmic confounding in recommendation algorithms through teacher-student learning simulations. Results highlight the need to account for emergent behaviors from interactions between people and algorithms.
  arXiv  Detail & Related papers  (2022-01-18T18:58:03Z)
• Safe Learning and Optimization Techniques: Towards a Survey of the State of the Art [3.6954802719347413]
  Safe learning and optimization deals with learning and optimization problems that avoid, as much as possible, the evaluation of non-safe input points. A comprehensive survey of safe reinforcement learning algorithms was published in 2015, but related works in active learning and in optimization were not considered. This paper reviews those algorithms from a number of domains including reinforcement learning, Gaussian process regression and classification, evolutionary algorithms, and active learning.
  arXiv  Detail & Related papers  (2021-01-23T13:58:09Z)
• Evolving Reinforcement Learning Algorithms [186.62294652057062]
  We propose a method for meta-learning reinforcement learning algorithms. The learned algorithms are domain-agnostic and can generalize to new environments not seen during training. We highlight two learned algorithms which obtain good generalization performance over other classical control tasks, gridworld type tasks, and Atari games.
  arXiv  Detail & Related papers  (2021-01-08T18:55:07Z)
• A Systematic Review on Context-Aware Recommender Systems using Deep Learning and Embeddings [0.0]
  Context-Aware Recommender Systems were created, accomplishing state-of-the-art results. Deep Learning and Embeddings techniques are being applied to improve Context-Aware Recommender Systems.
  arXiv  Detail & Related papers  (2020-07-09T13:23:40Z)
• Reinforcement Learning as Iterative and Amortised Inference [62.997667081978825]
  We use the control as inference framework to outline a novel classification scheme based on amortised and iterative inference. We show that taking this perspective allows us to identify parts of the algorithmic design space which have been relatively unexplored.
  arXiv  Detail & Related papers  (2020-06-13T16:10:03Z)
• Rethinking Few-Shot Image Classification: a Good Embedding Is All You Need? [72.00712736992618]
  We show that a simple baseline: learning a supervised or self-supervised representation on the meta-training set, outperforms state-of-the-art few-shot learning methods. An additional boost can be achieved through the use of self-distillation. We believe that our findings motivate a rethinking of few-shot image classification benchmarks and the associated role of meta-learning algorithms.
  arXiv  Detail & Related papers  (2020-03-25T17:58:42Z)
• Reward-Conditioned Policies [100.64167842905069]
  imitation learning requires near-optimal expert data. Can we learn effective policies via supervised learning without demonstrations? We show how such an approach can be derived as a principled method for policy search.
  arXiv  Detail & Related papers  (2019-12-31T18:07:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.