Empirical and Experimental Perspectives on Big Data in Recommendation Systems: A Comprehensive Survey

• URL: http://arxiv.org/abs/2402.03368v1
• Date: Thu, 1 Feb 2024 23:51:29 GMT
• Title: Empirical and Experimental Perspectives on Big Data in Recommendation Systems: A Comprehensive Survey
• Authors: Kamal Taha, Paul D. Yoo, Aya Taha
• Abstract summary: This survey paper provides a comprehensive analysis of big data algorithms in recommendation systems. It proposes a two-pronged approach: a thorough analysis of current algorithms and a novel, hierarchical taxonomy for precise categorization.
• Score: 2.6319554262325924
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This survey paper provides a comprehensive analysis of big data algorithms in recommendation systems, addressing the lack of depth and precision in existing literature. It proposes a two-pronged approach: a thorough analysis of current algorithms and a novel, hierarchical taxonomy for precise categorization. The taxonomy is based on a tri-level hierarchy, starting with the methodology category and narrowing down to specific techniques. Such a framework allows for a structured and comprehensive classification of algorithms, assisting researchers in understanding the interrelationships among diverse algorithms and techniques. Covering a wide range of algorithms, this taxonomy first categorizes algorithms into four main analysis types: User and Item Similarity-Based Methods, Hybrid and Combined Approaches, Deep Learning and Algorithmic Methods, and Mathematical Modeling Methods, with further subdivisions into sub-categories and techniques. The paper incorporates both empirical and experimental evaluations to differentiate between the techniques. The empirical evaluation ranks the techniques based on four criteria. The experimental assessments rank the algorithms that belong to the same category, sub-category, technique, and sub-technique. Also, the paper illuminates the future prospects of big data techniques in recommendation systems, underscoring potential advancements and opportunities for further research in this field

Related papers

• Comprehensive Review and Empirical Evaluation of Causal Discovery Algorithms for Numerical Data [3.9523536371670045]
  Causal analysis has become an essential component in understanding the underlying causes of phenomena across various fields. Existing literature on causal discovery algorithms is fragmented, with inconsistent methodologies. A lack of comprehensive evaluations, i.e., data characteristics are often ignored to be jointly analyzed when benchmarking algorithms.
  arXiv  Detail & Related papers  (2024-07-17T23:47:05Z)
• Empirical and Experimental Insights into Data Mining Techniques for Crime Prediction: A Comprehensive Survey [0.8702432681310399]
  The paper covers the statistical methods, machine learning algorithms, and deep learning techniques employed to analyze crime data. We propose a methodological taxonomy that classifies crime prediction algorithms into specific techniques.
  arXiv  Detail & Related papers  (2024-02-17T15:00:45Z)
• Text Classification: A Review, Empirical, and Experimental Evaluation [2.341806147715478]
  Existing survey papers categorize algorithms for text classification into broad classes. We introduce a novel methodological taxonomy that classifies algorithms hierarchically into fine-grained classes and specific techniques. Our study is the first survey to utilize this methodological taxonomy for classifying algorithms for text classification.
  arXiv  Detail & Related papers  (2024-01-11T08:17:42Z)
• Incremental hierarchical text clustering methods: a review [49.32130498861987]
  This study aims to analyze various hierarchical and incremental clustering techniques. The main contribution of this research is the organization and comparison of the techniques used by studies published between 2010 and 2018 that aimed to texts documents clustering.
  arXiv  Detail & Related papers  (2023-12-12T22:27:29Z)
• Beyond original Research Articles Categorization via NLP [2.28438857884398]
  The study leverages the power of pre-trained language models, specifically SciBERT, to extract meaningful representations of abstracts from the ArXiv dataset. The results demonstrate that the proposed approach captures subject information more effectively than the traditional arXiv labeling system.
  arXiv  Detail & Related papers  (2023-09-13T15:23:30Z)
• A Gold Standard Dataset for the Reviewer Assignment Problem [117.59690218507565]
  "Similarity score" is a numerical estimate of the expertise of a reviewer in reviewing a paper. Our dataset consists of 477 self-reported expertise scores provided by 58 researchers. For the task of ordering two papers in terms of their relevance for a reviewer, the error rates range from 12%-30% in easy cases to 36%-43% in hard cases.
  arXiv  Detail & Related papers  (2023-03-23T16:15:03Z)
• Deep Learning Schema-based Event Extraction: Literature Review and Current Trends [60.29289298349322]
  Event extraction technology based on deep learning has become a research hotspot. This paper fills the gap by reviewing the state-of-the-art approaches, focusing on deep learning-based models.
  arXiv  Detail & Related papers  (2021-07-05T16:32:45Z)
• A Survey on Deep Semi-supervised Learning [51.26862262550445]
  We first present a taxonomy for deep semi-supervised learning that categorizes existing methods. We then offer a detailed comparison of these methods in terms of the type of losses, contributions, and architecture differences.
  arXiv  Detail & Related papers  (2021-02-28T16:22:58Z)
• A Survey of Embedding Space Alignment Methods for Language and Knowledge Graphs [77.34726150561087]
  We survey the current research landscape on word, sentence and knowledge graph embedding algorithms. We provide a classification of the relevant alignment techniques and discuss benchmark datasets used in this field of research.
  arXiv  Detail & Related papers  (2020-10-26T16:08:13Z)
• A Survey on Text Classification: From Shallow to Deep Learning [83.47804123133719]
  The last decade has seen a surge of research in this area due to the unprecedented success of deep learning. This paper fills the gap by reviewing the state-of-the-art approaches from 1961 to 2021. We create a taxonomy for text classification according to the text involved and the models used for feature extraction and classification.
  arXiv  Detail & Related papers  (2020-08-02T00:09:03Z)

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.