Deep Learning feature selection to unhide demographic recommender systems factors

• URL: http://arxiv.org/abs/2006.12379v1
• Date: Wed, 17 Jun 2020 17:36:48 GMT
• Title: Deep Learning feature selection to unhide demographic recommender systems factors
• Authors: Jes\'us Bobadilla, \'Angel Gonz\'alez-Prieto, Fernando Ortega, Ra\'ul Lara-Cabrera
• Abstract summary: The matrix factorization model generates factors which do not incorporate semantic knowledge. DeepUnHide is able to extract demographic information from the users and items factors in collaborative filtering recommender systems.
• Score: 63.732639864601914
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Extracting demographic features from hidden factors is an innovative concept that provides multiple and relevant applications. The matrix factorization model generates factors which do not incorporate semantic knowledge. This paper provides a deep learning-based method: DeepUnHide, able to extract demographic information from the users and items factors in collaborative filtering recommender systems. The core of the proposed method is the gradient-based localization used in the image processing literature to highlight the representative areas of each classification class. Validation experiments make use of two public datasets and current baselines. Results show the superiority of DeepUnHide to make feature selection and demographic classification, compared to the state of art of feature selection methods. Relevant and direct applications include recommendations explanation, fairness in collaborative filtering and recommendation to groups of users.

Related papers

• A Deep Features-Based Approach Using Modified ResNet50 and Gradient Boosting for Visual Sentiments Classification [1.2434714657059942]
  This research develops a fusion of deep learning and machine learning algorithms. Deep feature-based method for multiclass classification has been used to extract deep features from modified ResNet50. gradient boosting algorithm has been used to classify photos containing emotional content.
  arXiv  Detail & Related papers  (2024-08-15T04:18:40Z)
• Greedy feature selection: Classifier-dependent feature selection via greedy methods [2.4374097382908477]
  The purpose of this study is to introduce a new approach to feature ranking for classification tasks, called in what follows greedy feature selection. The benefits of such scheme are investigated theoretically in terms of model capacity indicators, such as the Vapnik-Chervonenkis (VC) dimension or the kernel alignment.
  arXiv  Detail & Related papers  (2024-03-08T08:12:05Z)
• A Contrast Based Feature Selection Algorithm for High-dimensional Data set in Machine Learning [9.596923373834093]
  We propose a novel filter feature selection method, ContrastFS, which selects discriminative features based on the discrepancies features shown between different classes. We validate effectiveness and efficiency of our approach on several widely studied benchmark datasets, results show that the new method performs favorably with negligible computation.
  arXiv  Detail & Related papers  (2024-01-15T05:32:35Z)
• Graph-based Extreme Feature Selection for Multi-class Classification Tasks [7.863638253070439]
  This work focuses on a graph-based, filter feature selection method that is suited for multi-class classifications tasks. We aim to drastically reduce the number of selected features, in order to create a sketch of the original data that codes valuable information for the classification task.
  arXiv  Detail & Related papers  (2023-03-03T09:06:35Z)
• Deep networks for system identification: a Survey [56.34005280792013]
  System identification learns mathematical descriptions of dynamic systems from input-output data. Main aim of the identified model is to predict new data from previous observations. We discuss architectures commonly adopted in the literature, like feedforward, convolutional, and recurrent networks.
  arXiv  Detail & Related papers  (2023-01-30T12:38:31Z)
• Evaluation of Self-taught Learning-based Representations for Facial Emotion Recognition [62.30451764345482]
  This work describes different strategies to generate unsupervised representations obtained through the concept of self-taught learning for facial emotion recognition. The idea is to create complementary representations promoting diversity by varying the autoencoders' initialization, architecture, and training data. Experimental results on Jaffe and Cohn-Kanade datasets using a leave-one-subject-out protocol show that FER methods based on the proposed diverse representations compare favorably against state-of-the-art approaches.
  arXiv  Detail & Related papers  (2022-04-26T22:48:15Z)
• Visualization of Supervised and Self-Supervised Neural Networks via Attribution Guided Factorization [87.96102461221415]
  We develop an algorithm that provides per-class explainability. In an extensive battery of experiments, we demonstrate the ability of our methods to class-specific visualization.
  arXiv  Detail & Related papers  (2020-12-03T18:48:39Z)
• Generating Categories for Sets of Entities [34.32017697099142]
  Category systems are central components of knowledge bases, as they provide a hierarchical grouping of semantically related concepts and entities. This paper presents a method of generating categories for sets of entities using neural abstractive summarization models. We develop a test collection based on Wikipedia categories and demonstrate the effectiveness of the proposed approach.
  arXiv  Detail & Related papers  (2020-08-19T13:31:07Z)
• Saliency-driven Class Impressions for Feature Visualization of Deep Neural Networks [55.11806035788036]
  It is advantageous to visualize the features considered to be essential for classification. Existing visualization methods develop high confidence images consisting of both background and foreground features. In this work, we propose a saliency-driven approach to visualize discriminative features that are considered most important for a given task.
  arXiv  Detail & Related papers  (2020-07-31T06:11:06Z)
• Sequential Recommendation with Self-Attentive Multi-Adversarial Network [101.25533520688654]
  We present a Multi-Factor Generative Adversarial Network (MFGAN) for explicitly modeling the effect of context information on sequential recommendation. Our framework is flexible to incorporate multiple kinds of factor information, and is able to trace how each factor contributes to the recommendation decision over time.
  arXiv  Detail & Related papers  (2020-05-21T12:28:59Z)

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.