Critically Examining the Claimed Value of Convolutions over User-Item Embedding Maps for Recommender Systems

• URL: http://arxiv.org/abs/2007.11893v2
• Date: Wed, 5 Aug 2020 18:53:28 GMT
• Title: Critically Examining the Claimed Value of Convolutions over User-Item Embedding Maps for Recommender Systems
• Authors: Maurizio Ferrari Dacrema, Federico Parroni, Paolo Cremonesi, Dietmar Jannach
• Abstract summary: In recent years, algorithm research in the area of recommender systems has shifted from matrix factorization techniques to neural approaches. We show through analytical considerations and empirical evaluations that the claimed gains reported in the literature cannot be attributed to the ability of CNNs to model embedding correlations.
• Score: 14.414055798999764
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, algorithm research in the area of recommender systems has shifted from matrix factorization techniques and their latent factor models to neural approaches. However, given the proven power of latent factor models, some newer neural approaches incorporate them within more complex network architectures. One specific idea, recently put forward by several researchers, is to consider potential correlations between the latent factors, i.e., embeddings, by applying convolutions over the user-item interaction map. However, contrary to what is claimed in these articles, such interaction maps do not share the properties of images where Convolutional Neural Networks (CNNs) are particularly useful. In this work, we show through analytical considerations and empirical evaluations that the claimed gains reported in the literature cannot be attributed to the ability of CNNs to model embedding correlations, as argued in the original papers. Moreover, additional performance evaluations show that all of the examined recent CNN-based models are outperformed by existing non-neural machine learning techniques or traditional nearest-neighbor approaches. On a more general level, our work points to major methodological issues in recommender systems research.

Related papers

• SoK: On Finding Common Ground in Loss Landscapes Using Deep Model Merging Techniques [4.013324399289249]
  We present a novel taxonomy of model merging techniques organized by their core algorithmic principles. We distill repeated empirical observations from the literature in these fields into characterizations of four major aspects of loss landscape geometry.
  arXiv  Detail & Related papers  (2024-10-16T18:14:05Z)
• Visual Prompting Upgrades Neural Network Sparsification: A Data-Model Perspective [64.04617968947697]
  We introduce a novel data-model co-design perspective: to promote superior weight sparsity. Specifically, customized Visual Prompts are mounted to upgrade neural Network sparsification in our proposed VPNs framework.
  arXiv  Detail & Related papers  (2023-12-03T13:50:24Z)
• EvalAttAI: A Holistic Approach to Evaluating Attribution Maps in Robust and Non-Robust Models [0.3425341633647624]
  This paper focuses on evaluating methods of attribution mapping to find whether robust neural networks are more explainable. We propose a new explainability faithfulness metric (called EvalAttAI) that addresses the limitations of prior metrics.
  arXiv  Detail & Related papers  (2023-03-15T18:33:22Z)
• 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)
• Temporal Relevance Analysis for Video Action Models [70.39411261685963]
  We first propose a new approach to quantify the temporal relationships between frames captured by CNN-based action models. We then conduct comprehensive experiments and in-depth analysis to provide a better understanding of how temporal modeling is affected.
  arXiv  Detail & Related papers  (2022-04-25T19:06:48Z)
• Gone Fishing: Neural Active Learning with Fisher Embeddings [55.08537975896764]
  There is an increasing need for active learning algorithms that are compatible with deep neural networks. This article introduces BAIT, a practical representation of tractable, and high-performing active learning algorithm for neural networks.
  arXiv  Detail & Related papers  (2021-06-17T17:26:31Z)
• A Survey on Neural Recommendation: From Collaborative Filtering to Content and Context Enriched Recommendation [70.69134448863483]
  Research in recommendation has shifted to inventing new recommender models based on neural networks. In recent years, we have witnessed significant progress in developing neural recommender models.
  arXiv  Detail & Related papers  (2021-04-27T08:03:52Z)
• A Detailed Look At CNN-based Approaches In Facial Landmark Detection [5.774786149181392]
  CNN-based approaches can be divided into regression and heatmap approaches. In this paper, we investigate both CNN-based approaches, generalize their advantages and disadvantages, and introduce a variation of the heatmap approach. A comprehensive evaluation is conducted and the result shows that the proposed model outperforms other models in all tested datasets.
  arXiv  Detail & Related papers  (2020-05-08T16:17:42Z)
• Amortized Bayesian Inference for Models of Cognition [0.1529342790344802]
  Recent advances in simulation-based inference using specialized neural network architectures circumvent many previous problems of approximate Bayesian computation. We provide a general introduction to amortized Bayesian parameter estimation and model comparison.
  arXiv  Detail & Related papers  (2020-05-08T08:12:15Z)
• Rethinking Generalization of Neural Models: A Named Entity Recognition Case Study [81.11161697133095]
  We take the NER task as a testbed to analyze the generalization behavior of existing models from different perspectives. Experiments with in-depth analyses diagnose the bottleneck of existing neural NER models. As a by-product of this paper, we have open-sourced a project that involves a comprehensive summary of recent NER papers.
  arXiv  Detail & Related papers  (2020-01-12T04:33:53Z)

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.