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Updated: Nov 14, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

Attribute-Aware Recommender System Based on Collaborative Filtering: Survey and Classification.

Wen-Hao Chen¹, Chin-Chi Hsu², Yi-An Lai¹

¹Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan.

Frontiers in Big Data

|March 11, 2021

Summary

This summary is machine-generated.

This survey categorizes attribute-aware collaborative filtering (CF) models into four mathematical types. It offers mathematical insights and experimental comparisons of these advanced recommendation systems.

Keywords:

attribute collaborative filtering matrix factorization recommender system survey

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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Last Updated: Nov 14, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

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Published on: December 15, 2023

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Collaborative Filtering (CF) models predict user preferences based on historical data.
Attribute-aware CF models enhance predictions by incorporating user, item, and rating attributes.
A comprehensive survey of attribute-aware CF research over the past decade is needed.

Purpose of the Study:

To survey and categorize attribute-aware CF models developed in the last decade.
To provide a mathematical framework for understanding these models.
To compare the effectiveness of different attribute-aware CF model categories.

Main Methods:

Literature review of attribute-aware CF systems.
Mathematical classification of identified models into four distinct categories.
Experimental evaluation of representative models from each category.

Main Results:

Attribute-aware CF models were successfully classified into four mathematical categories.
Mathematical interpretations and insights were provided for each category.
Experimental results demonstrated the comparative effectiveness of major models within each category.

Conclusions:

Attribute-aware CF models offer significant improvements in rating prediction accuracy.
The proposed categorization provides a valuable framework for future research in this field.
Empirical comparisons highlight the strengths and weaknesses of different modeling approaches.