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Mathematics
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State-of-the-Art Mathematical Applications in Asia-Pacific Area
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State-of-the-Art Mathematical Applications in Asia-Pacific Area

A special issue of Mathematics (ISSN 2227-7390).

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 2447

Special Issue Editors

Prof. Dr. Tadashi Dohi

E-Mail Website
Guest Editor
Graduate School of Advanced Science and Engineering, School of Informatics and Data Sciences, Hiroshima University, 1-4-1 Kagamiyama, Higashihiroshima 7398527, Japan
Interests: stochastic model; reliability and maintenance; performance evaluation
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. William Guo

E-Mail Website
Guest Editor
School of Engineering and Technology, Central Queensland University, North Rockhampton, QLD 4702, Australia
Interests: mathematics education; computational intelligence; data mining; modelling and simulation; geophysics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Asia is one of the most powerful economic engines in the world, which has partly been fueled by the rapid growth in the vast investment in research and development, and in turn, the outcomes have become a significant source of publication in international journals, including broad applied mathematics.

The recent development of computer technology enables us to solve complex problems by applying sophisticated algorithms and high-power computation resources. However, applied mathematics is still important in analyzing and formulating the procedures for and evaluating the outcomes from such operations. For example, many engineering systems, including mechanical, electrical, computer, chemical, robotic, and control systems, can be described by mathematical models. In manufacturing, transportation, and many other industries, operations research, sophisticated optimization algorithms, and stochastic modeling and simulation play a central role in achieving the best possible solutions.

This Special Issue provides an opportunity for scientists, engineers, and researchers to share their latest research outcomes related to applied mathematics with real world-applications. Contributions can be from either a co-author from an Asian country/region, or an application-oriented project based in an Asian country/region. We expect both mathematical contributions and their applications to deal with a ‘real problem' with solid evidence. The topics of interest include but are not limited to:

  • Differential equations;
  • Linear algebra;
  • Probability and statistics;
  • Mechanical engineering;
  • Robotics;
  • Electrical and electronic engineering;
  • Control and automation;
  • Industrial engineering;
  • Chemical engineering;
  • Computer engineering;
  • Communication networks;
  • Biology and bioinformatics;
  • Reliability and safety engineering;
  • Aviation and transportation;
  • Operations research;
  • Management science;
  • Machine learning and data science.

Prof. Dr. Tadashi Dohi
Prof. Dr. William Guo
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (1 paper)

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Research

20 pages, 877 KiB  
Article
Enhanced Graph Learning for Recommendation via Causal Inference
by Suhua Wang, Hongjie Ji, Minghao Yin, Yuling Wang, Mengzhu Lu and Hui Sun
Mathematics 2022, 10(11), 1881; https://doi.org/10.3390/math10111881 - 31 May 2022
Viewed by 1738
Abstract
The goal of the recommender system is to learn the user’s preferences from the entity (user–item) historical interaction data, so as to predict the user’s ratings on new items or recommend new item sequences to users. There are two major challenges: (1) Datasets [...] Read more.
The goal of the recommender system is to learn the user’s preferences from the entity (user–item) historical interaction data, so as to predict the user’s ratings on new items or recommend new item sequences to users. There are two major challenges: (1) Datasets are usually sparse. The item side is often accompanied by some auxiliary information, such as attributes or context; it can help to slightly improve its representation. However, the user side is usually presented in the form of ID due to personal privacy. (2) Due to the influences of confounding factors, such as the popularity of items, users’ ratings on items often have bias that cannot be recognized by the traditional recommendation methods. In order to solve these two problems, in this paper, (1) we explore the use of a graph model to fuse the interactions between users and common rating items, that is, incorporating the “neighbor” information into the target user to enrich user representations; (2) the do() operator is used to deduce the causality after removing the influences of confounding factors, rather than the correlation of the data surface fitted by traditional machine learning. We propose the EGCI model, i.e., enhanced graph learning for recommendation via causal inference. The model embeds user relationships and item attributes into the latent semantic space to obtain high-quality user and item representations. In addition, the mixed bias implied in the rating process is calibrated by considering the popularity of items. Experimental results on three real-world datasets show that EGCI is significantly better than the baselines. Full article
(This article belongs to the Special Issue State-of-the-Art Mathematical Applications in Asia-Pacific Area)
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