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Chin-Yi Chen and Jih-Jeng Huang
Traditional movie recommendation systems are increasingly falling short in the contemporary landscape of abundant information and evolving user behaviors. This study introduced the temporal knowledge graph recommender system (TKGRS), a ground-breaking al...
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Tiago P. Pagano, Rafael B. Loureiro, Fernanda V. N. Lisboa, Gustavo O. R. Cruz, Rodrigo M. Peixoto, Guilherme A. de Sousa Guimarães, Ewerton L. S. Oliveira, Ingrid Winkler and Erick G. Sperandio Nascimento
The majority of current approaches for bias and fairness identification or mitigation in machine learning models are applications for a particular issue that fails to account for the connection between the application context and its associated sensitive...
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Tan Nghia Duong, Nguyen Nam Doan, Truong Giang Do, Manh Hoang Tran, Duc Minh Nguyen and Quang Hieu Dang
Recommendation systems based on convolutional neural network (CNN) have attracted great attention due to their effectiveness in processing unstructured data such as images or audio. However, a huge amount of raw data produced by data crawling and digital...
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Tae-Gyu Hwang and Sung Kwon Kim
A recommender system (RS) refers to an agent that recommends items that are suitable for users, and it is implemented through collaborative filtering (CF). CF has a limitation in improving the accuracy of recommendations based on matrix factorization (MF...
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Haiyan Xu, Yanhui Ding, Jing Sun, Kun Zhao and Yuanjian Chen
Group recommendation has attracted significant research efforts for its importance in benefiting group members. The purpose of group recommendation is to provide recommendations to group users, such as recommending a movie to several friends. Group recom...
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Mingxuan Sun, Fei Li and Jian Zhang
Collaborative filtering (CF) approaches, which provide recommendations based on ratings or purchase history, perform well for users and items with sufficient interactions. However, CF approaches suffer from the cold-start problem for users and items with...
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