Restricted Bernoulli Matrix Factorization: Balancing the trade-off between prediction accuracy and coverage in classification based collaborative filtering

• URL: http://arxiv.org/abs/2210.10619v2
• Date: Thu, 21 Dec 2023 17:18:44 GMT
• Title: Restricted Bernoulli Matrix Factorization: Balancing the trade-off between prediction accuracy and coverage in classification based collaborative filtering
• Authors: \'Angel Gonz\'alez-Prieto and Abraham Guti\'errez and Fernando Ortega and Ra\'ul Lara-Cabrera
• Abstract summary: We propose Restricted Bernoulli Matrix Factorization (ResBeMF) to enhance the performance of classification-based collaborative filtering. The proposed model provides a good balance in terms of the quality measures used compared to other recommendation models.
• Score: 45.335821132209766
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reliability measures associated with the prediction of the machine learning models are critical to strengthening user confidence in artificial intelligence. Therefore, those models that are able to provide not only predictions, but also reliability, enjoy greater popularity. In the field of recommender systems, reliability is crucial, since users tend to prefer those recommendations that are sure to interest them, that is, high predictions with high reliabilities. In this paper, we propose Restricted Bernoulli Matrix Factorization (ResBeMF), a new algorithm aimed at enhancing the performance of classification-based collaborative filtering. The proposed model has been compared to other existing solutions in the literature in terms of prediction quality (Mean Absolute Error and accuracy scores), prediction quantity (coverage score) and recommendation quality (Mean Average Precision score). The experimental results demonstrate that the proposed model provides a good balance in terms of the quality measures used compared to other recommendation models.

Related papers

• Self-Evolutionary Large Language Models through Uncertainty-Enhanced Preference Optimization [9.618391485742968]
  Iterative preference optimization has recently become one of the de-facto training paradigms for large language models (LLMs) We present an uncertainty-enhanced textbfPreference textbfOptimization framework to make the LLM self-evolve with reliable feedback. Our framework substantially alleviates the noisy problem and improves the performance of iterative preference optimization.
  arXiv  Detail & Related papers  (2024-09-17T14:05:58Z)
• Probabilistic Scores of Classifiers, Calibration is not Enough [0.32985979395737786]
  In binary classification tasks, accurate representation of probabilistic predictions is essential for various real-world applications. In this study, we highlight approaches that prioritize the alignment between predicted scores and true probability distributions. Our findings reveal limitations in traditional calibration metrics, which could undermine the reliability of predictive models for critical decision-making.
  arXiv  Detail & Related papers  (2024-08-06T19:53:00Z)
• Towards Calibrated Deep Clustering Network [60.71776081164377]
  In deep clustering, the estimated confidence for a sample belonging to a particular cluster greatly exceeds its actual prediction accuracy. We propose a novel dual-head (calibration head and clustering head) deep clustering model that can effectively calibrate the estimated confidence and the actual accuracy. Extensive experiments demonstrate the proposed calibrated deep clustering model not only surpasses state-of-the-art deep clustering methods by 10 times in terms of expected calibration error but also significantly outperforms them in terms of clustering accuracy.
  arXiv  Detail & Related papers  (2024-03-04T11:23:40Z)
• Variational Factorization Machines for Preference Elicitation in Large-Scale Recommender Systems [17.050774091903552]
  We propose a variational formulation of factorization machines (FMs) that can be easily optimized using standard mini-batch descent gradient. Our algorithm learns an approximate posterior distribution over the user and item parameters, which leads to confidence intervals over the predictions. We show, using several datasets, that it has comparable or better performance than existing methods in terms of prediction accuracy.
  arXiv  Detail & Related papers  (2022-12-20T00:06:28Z)
• Calibrated Selective Classification [34.08454890436067]
  We develop a new approach to selective classification in which we propose a method for rejecting examples with "uncertain" uncertainties. We present a framework for learning selectively calibrated models, where a separate selector network is trained to improve the selective calibration error of a given base model. We demonstrate the empirical effectiveness of our approach on multiple image classification and lung cancer risk assessment tasks.
  arXiv  Detail & Related papers  (2022-08-25T13:31:09Z)
• Test-time Collective Prediction [73.74982509510961]
  Multiple parties in machine learning want to jointly make predictions on future test points. Agents wish to benefit from the collective expertise of the full set of agents, but may not be willing to release their data or model parameters. We explore a decentralized mechanism to make collective predictions at test time, leveraging each agent's pre-trained model.
  arXiv  Detail & Related papers  (2021-06-22T18:29:58Z)
• Trust but Verify: Assigning Prediction Credibility by Counterfactual Constrained Learning [123.3472310767721]
  Prediction credibility measures are fundamental in statistics and machine learning. These measures should account for the wide variety of models used in practice. The framework developed in this work expresses the credibility as a risk-fit trade-off.
  arXiv  Detail & Related papers  (2020-11-24T19:52:38Z)
• Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift [100.52588638477862]
  We develop an approximate Bayesian inference scheme based on posterior regularisation. We demonstrate the utility of our method in the context of transferring prognostic models of prostate cancer across globally diverse populations.
  arXiv  Detail & Related papers  (2020-06-26T13:50:19Z)
• Providing reliability in Recommender Systems through Bernoulli Matrix Factorization [63.732639864601914]
  This paper proposes Bernoulli Matrix Factorization (BeMF) to provide both prediction values and reliability values. BeMF acts on model-based collaborative filtering rather than on memory-based filtering. The more reliable a prediction is, the less liable it is to be wrong.
  arXiv  Detail & Related papers  (2020-06-05T14:24:27Z)

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.