Editorial for Vol.32, No.3 by Alan Jović

“…Journal of Computing and Information Technology brings four papers from the areas of power load forecasting, fault diagnosis, recommender systems, and biomedical data mining.…”
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Music Personalized Label Clustering and Recommendation Visualization by Yongkang Huo

“…First, this paper describes the main ideas and methods used in current recommendation systems and summarizes the areas that need attention and consideration. …”
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Key Technologies and Discrete Dynamic Modeling Analysis of Online Travel Planning System Based on Big Data Scenario Aware Service by Yange Hao, Na Song

“…The key technology of online travel recommendation system has been widely concerned by many Internet experts. …”
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Hierarchical Learning: A Hybrid of Federated Learning and Personalization Fine-Tuning by Li Shuyi, Zhang Bairong

“…This hybrid approach not only enhances model accuracy but also preserves data privacy and increases scalability, making it a promising solution for decentralized recommendation systems.…”
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Joint embedding–classifier learning for interpretable collaborative filtering by Clémence Réda, Jill-Jênn Vie, Olaf Wolkenhauer

“…Abstract Background Interpretability is a topical question in recommender systems, especially in healthcare applications. …”
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Implementation Of MAUT Method for Making Website-based Tourism Recommendations in Yogyakarta based on Maps Location by M. Nuraminudin, Melany Mustika Dewi, Akhmad Dahlan

“…The rapid growth of tourism demands the integration of information technology, especially in the development of recommendation systems, to improve visitor experience and satisfaction and support sustainable destination management. …”
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Invariant Representation Learning in Multimedia Recommendation with Modality Alignment and Model Fusion by Xinghang Hu, Haiteng Zhang

“…Multimedia recommendation systems aim to accurately predict user preferences from multimodal data. …”
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An Improved Recommendation Method Based on Content Filtering and Collaborative Filtering by Lei Fu, XiaoMing Ma

“…The emergence and application of the network marketing recommendation system have greatly improved this series of problems. …”
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Exercise Recommendation Model Based on Cognitive Level and Educational Big Data Mining by Yongming Pu, Hongming Chen

“…Compared with other recommendation systems, this model has higher accuracy and recommendation effect.…”
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Big Data Improving Recommendation Quality in Music Applications by Yuzhong Zhang, Ai Fang, Duo Jin, Liyu Yuan

“…The sparsity and overlapping and reliability was applied to adjust recommendation algorithm, combined soaring words and content labels and filtering rules with mixing recommendation to resolve the common problems of recommendation system.…”
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Sistem Rekomendasi Profesi Berdasarkan Dimensi Big Five Personality Menggunakan Fuzzy Inference System Tsukamoto by Farhanna Mar'i, Wayan Firdaus Mahmudy, Cleoputri Yusainy

“…In a company, a professional recommendation system can be used to place an employee in the right position. …”
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Enhancing Agricultural Productivity: A Machine Learning Approach to Crop Recommendations by Farida Siddiqi Prity, MD. Mehadi Hasan, Shakhawat Hossain Saif, Md. Maruf Hossain, Sazzad Hossain Bhuiyan, Md. Ariful Islam, Md Tousif Hasan Lavlu

“…Therefore, this paper aims to present a Machine Learning (ML) based crop recommendation system tailored for the farming landscape. …”
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Optimizing personalized psychological well-being interventions through digital phenotyping: results from a randomized non-clinical trial by Giulia Rocchi, Giulia Rocchi, Emanuela Vocaj, Simone Moawad, Alessandro Antonucci, Carlo Grigioni, Vincenzo Giuffrida, Joy Bordini

“…A clustering algorithm created a user profile and content recommendation system to provide personalized exercises based on users’ responses.ResultsFour distinct clusters of participants emerged, based on factors such as online alerts, social media use, insomnia, attention and energy levels. …”
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Research on E-Commerce Platform-Based Personalized Recommendation Algorithm by Zhijun Zhang, Gongwen Xu, Pengfei Zhang

“…In the recommendation prediction stage, considering timeliness of the recommendation system, time weighted based recommendation prediction formula is adopted to design a personalized recommendation model by integrating level filling method and rating time. …”
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Personalized fund recommendation with dynamic utility learning by Jiaxin Wei, Jia Liu

“…Abstract This study introduces a fund recommendation system based on the $$\epsilon$$ ϵ -greedy algorithm and an incremental learning framework. …”
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Researcb on Social Network Collaborative Filtering Based E-commerce Recommending by Chunhua Ju, Fuguang Bao, Chonghuan Xu

“…Therefore, an E-commerce recommending system based on social network collaborative filtering was proposed. …”
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Object detection and multimodal learning for product recommendations by Karolina Selwon, Paweł Wnuk

“… This study showcases how deep learning can be applied to automated information extraction in fashion data to create a recommendation system. The proposed approach is an algorithm for recommending multiple products based on visual and textual features, ensuring compatibility with query items. …”
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Collaborative filtering recommendation algorithm based on rough set rule extraction by Yonggong REN, Yunpeng ZHANG, Zhipeng ZHANG

“…To address the problem that in a practical recommendation system (RS),because of the datasets are often very sparse,the traditional collaborative filtering (CF) approach cannot provide recommendations with higher quality,a novel CF based on rough set rule extraction was proposed.Firstly,the attributes of user/item and the user-item rating matrix were used to construct a decision table.Then,the core value of each rule in the table was extracted through using the decision table reduction algorithm.Finally,according to the nuclear value decision rule of the core value table,the reductions of all decision rules were utilized to predict the rating scores of un-rated items.Experimental results suggest that the proposed approach can alleviate the data sparsity problem of CF,and provide recommendations with higher accuracy.…”
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POI recommendation by incorporating trust-distrust relationship in LBSN by Jinghua ZHU, Qian MING

“…POI (point of interest) recommendation is an important personalized service in the LBSN (location-based social network) which has wide applications such as popular sights recommendation and travel routes planning.Most existing collaborative filter algorithms make recommendation according to user similarity and location similarity,they don’t consider the trust relationship between users.And trust relationship is helpful to improve recommendation accuracy,robustness and user satisfaction.Firstly,the propagation property of trust and distrust relationship was analyzed.Then,the measurement and computation method of trust were given.Finally,a hybrid recommendation system which combined user similarity,geographical location similarity and trust relationship was proposed.The experiments results show that the hybrid recommendation is obviously superior to the traditional collaborative filtering in terms of results accuracy and user satisfaction.…”
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Personalized Clothing Recommendation Based on User Emotional Analysis by Xueping Su, Meng Gao, Jie Ren, Yunhong Li, Matthias Rätsch

“…However, the recommendation quality of the existing clothing recommendation system is not enough to meet the user’s needs. …”
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