Improvements on Recommender System based on Mathematical Principles

• URL: http://arxiv.org/abs/2304.13579v1
• Date: Wed, 26 Apr 2023 14:13:46 GMT
• Title: Improvements on Recommender System based on Mathematical Principles
• Authors: Fu Chen, Junkang Zou, Lingfeng Zhou, Zekai Xu, Zhenyu Wu
• Abstract summary: 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.
• Score: 10.027420333081084
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this article, we will research the Recommender System's implementation about how it works and the algorithms used. 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. Both the weakness and the strength of two different mathematical distance used to describe the similarity will be detailed illustrated in this article.

Related papers

• 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)
• A Novel Ranking Scheme for the Performance Analysis of Stochastic Optimization Algorithms using the Principles of Severity [9.310464457958844]
  We provide a novel ranking scheme to rank the algorithms over multiple single-objective optimization problems. The results of the algorithms are compared using a robust bootstrapping-based hypothesis testing procedure.
  arXiv  Detail & Related papers  (2024-05-31T19:35:34Z)
• Improving Recommendation Relevance by simulating User Interest [77.34726150561087]
  We observe that recommendation "recency" can be straightforwardly and transparently maintained by iterative reduction of ranks of inactive items. The basic idea behind this work is patented in a context of online recommendation systems.
  arXiv  Detail & Related papers  (2023-02-03T03:35:28Z)
• Multivariate Systemic Risk Measures and Computation by Deep Learning Algorithms [63.03966552670014]
  We discuss the key related theoretical aspects, with a particular focus on the fairness properties of primal optima and associated risk allocations. The algorithms we provide allow for learning primals, optima for the dual representation and corresponding fair risk allocations.
  arXiv  Detail & Related papers  (2023-02-02T22:16:49Z)
• Efficient computation of the Knowledge Gradient for Bayesian Optimization [1.0497128347190048]
  One-shot Hybrid KG is a new approach that combines several of the previously proposed ideas and is cheap to compute as well as powerful and efficient. All experiments are implemented in BOTorch and show empirically drastically reduced computational overhead with equal or improved performance.
  arXiv  Detail & Related papers  (2022-09-30T10:39:38Z)
• GHRS: Graph-based Hybrid Recommendation System with Application to Movie Recommendation [0.0]
  We propose a recommender system method using a graph-based model associated with the similarity of users' ratings. By utilizing the advantages of Autoencoder feature extraction, we extract new features based on all combined attributes. The experimental results on the MovieLens dataset show that the proposed algorithm outperforms many existing recommendation algorithms on recommendation accuracy.
  arXiv  Detail & Related papers  (2021-11-06T10:47:45Z)
• Optimized Recommender Systems with Deep Reinforcement Learning [0.0]
  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.
  arXiv  Detail & Related papers  (2021-10-06T19:54:55Z)
• Provably Faster Algorithms for Bilevel Optimization [54.83583213812667]
  Bilevel optimization has been widely applied in many important machine learning applications. We propose two new algorithms for bilevel optimization. We show that both algorithms achieve the complexity of $mathcalO(epsilon-1.5)$, which outperforms all existing algorithms by the order of magnitude.
  arXiv  Detail & Related papers  (2021-06-08T21:05:30Z)
• Estimating leverage scores via rank revealing methods and randomization [50.591267188664666]
  We study algorithms for estimating the statistical leverage scores of rectangular dense or sparse matrices of arbitrary rank. Our approach is based on combining rank revealing methods with compositions of dense and sparse randomized dimensionality reduction transforms.
  arXiv  Detail & Related papers  (2021-05-23T19:21:55Z)
• 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)
• Information Theoretic Meta Learning with Gaussian Processes [74.54485310507336]
  We formulate meta learning using information theoretic concepts; namely, mutual information and the information bottleneck. By making use of variational approximations to the mutual information, we derive a general and tractable framework for meta learning.
  arXiv  Detail & Related papers  (2020-09-07T16:47:30Z)

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.