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Jaroslaw Kurek, Tomasz Latkowski, Michal Bukowski, Bartosz Swiderski, Mateusz Lepicki, Grzegorz Baranik, Bogusz Nowak, Robert Zakowicz and Lukasz Dobrakowski
In the evolving realities of recruitment, the precision of job?candidate matching is crucial. This study explores the application of Zero-Shot Recommendation AI Models to enhance this matching process. Utilizing advanced pretrained models such as all-Min...
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Songlin Tian, Ying Yang and Lei Yang
Business intelligence (BI), as a system for business data integration, processing, and analysis, is receiving increasing attention from enterprises. Data visualization is an important feature of BI, which allows users to visually observe the distribution...
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Xiu Li, Aron Henriksson, Martin Duneld, Jalal Nouri and Yongchao Wu
Educational content recommendation is a cornerstone of AI-enhanced learning. In particular, to facilitate navigating the diverse learning resources available on learning platforms, methods are needed for automatically linking learning materials, e.g., in...
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Ming-Yen Lin, Ping-Chun Wu and Sue-Chen Hsueh
This study introduces session-aware recommendation models, leveraging GRU (Gated Recurrent Unit) and attention mechanisms for advanced latent interaction data integration. A primary advancement is enhancing latent context, a critical factor for boosting ...
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Deepanjal Shrestha, Tan Wenan, Deepmala Shrestha, Neesha Rajkarnikar and Seung-Ryul Jeong
This study introduces a data-driven and machine-learning approach to design a personalized tourist recommendation system for Nepal. It examines key tourist attributes, such as demographics, behaviors, preferences, and satisfaction, to develop four sub-mo...
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Abubaker Salem Mohamed Shibani, Masnizah Mohd, Ahmad Tarmizi Abdul Ghani, Mohamad Shanudin Zakaria and Sumaia Mohammed Al-Ghuribi
E-learning is an innovative strategy for enhancing teaching and learning in digital environments with the goal of enhancing education. In the same context, recommendation models have been developed for predicting the user?s learning preferences. A task t...
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Wei Chen, Yihao Zhang, Yantuan Xian and Yonghua Wen
Tremendous academic articles face serious information overload problems while supporting literature searches. Finding a research article in a relevant domain that meets researchers? requirements is challenging. Hence, different paper recommendation model...
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Yixin Huang, Rishi Srivastava, Chloe Ngo, Jerry Gao, Jane Wu and Sen Chiao
Food shortage issues affect more and more of the population globally as a consequence of the climate crisis, wars, and the COVID-19 pandemic. Increasing crop output has become one of the urgent priorities for many countries. To raise the productivity of ...
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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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Ji-Yoon Kim and Chae-Kwan Lim
The electronic publication market is growing along with the electronic commerce market. Electronic publishing companies use recommendation systems to increase sales to recommend various services to consumers. However, due to data sparsity, the recommenda...
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