4.6 Article

Deep Learning-Based Context-Aware Recommender System Considering Contextual Features

Related references

Note: Only part of the references are listed.
Article Automation & Control Systems

An Autoencoder Framework With Attention Mechanism for Cross-Domain Recommendation

Shi-Ting Zhong et al.

Summary: This article proposes a novel autoencoder framework with an attention mechanism (AAM) for cross-domain recommendation, which addresses the sparsity and cold-start problems in recommender systems. Experimental results show that the proposed method outperforms existing methods in cross-domain recommendation and AAM++ performs better than AAM on sparse and large-scale datasets.

IEEE TRANSACTIONS ON CYBERNETICS (2022)

Article Computer Science, Information Systems

Learning social representations with deep autoencoder for recommender system

Yiteng Pan et al.

WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS (2020)

Article Chemistry, Multidisciplinary

Deep Learning Architecture for Collaborative Filtering Recommender Systems

Jesus Bobadilla et al.

APPLIED SCIENCES-BASEL (2020)

Article Computer Science, Artificial Intelligence

A novel tourism recommender system in the context of social commerce

Leila Esmaeili et al.

EXPERT SYSTEMS WITH APPLICATIONS (2020)

Review Mathematics, Interdisciplinary Applications

Machine-Learning Methods for Computational Science and Engineering

Michael Frank et al.

COMPUTATION (2020)

Article Computer Science, Artificial Intelligence

Graph-based context-aware collaborative filtering

Tu Minh Phuong et al.

EXPERT SYSTEMS WITH APPLICATIONS (2019)

Article Computer Science, Theory & Methods

Deep Learning Based Recommender System: A Survey and New Perspectives

Shuai Zhang et al.

ACM COMPUTING SURVEYS (2019)

Article Computer Science, Information Systems

Customer Reviews Analysis With Deep Neural Networks for E-Commerce Recommender Systems

Babak Maleki Shoja et al.

IEEE ACCESS (2019)

Proceedings Paper Computer Science, Artificial Intelligence

Multi-Pointer Co-Attention Networks for Recommendation

Yi Tay et al.

KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING (2018)

Article Computer Science, Information Systems

A Survey of Recommender Systems Based on Deep Learning

Ruihui Mu

IEEE ACCESS (2018)

Proceedings Paper Automation & Control Systems

Matrix Factorization Model in Collaborative Filtering Algorithms: A Survey

Dheeraj Bokde et al.

PROCEEDINGS OF 4TH INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTING, COMMUNICATION AND CONTROL(ICAC3'15) (2015)

Article Computer Science, Cybernetics

Experimental evaluation of context-dependent collaborative filtering using item splitting

Linas Baltrunas et al.

USER MODELING AND USER-ADAPTED INTERACTION (2014)

Article Computer Science, Interdisciplinary Applications

Context-Aware Recommender Systems for Learning: A Survey and Future Challenges

Katrien Verbert et al.

IEEE TRANSACTIONS ON LEARNING TECHNOLOGIES (2012)

Article Computer Science, Hardware & Architecture

MATRIX FACTORIZATION TECHNIQUES FOR RECOMMENDER SYSTEMS

Yehuda Koren et al.

COMPUTER (2009)