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Business Intelligence and Big Data in E-commerce
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Business Intelligence and Big Data in E-commerce

A special issue of Big Data and Cognitive Computing (ISSN 2504-2289).

Deadline for manuscript submissions: 30 November 2024 | Viewed by 1170

Special Issue Editors

Prof. Dr. George Stalidis

E-Mail Website
Guest Editor
Department of Organization Management, Marketing and Tourism, International Hellenic University, Thessaloniki, Greece
Interests: business intelligence; knowledge modeling; multidimensional data analysis; recommendation systems
Dr. Dimitrios Kardaras

E-Mail Website
Guest Editor
Department of Business Administration, School of Business, Athens University of Economics and Business (AUEB), Athens, Greece
Interests: strategic information systems; services customisation; fuzzy cognitive maps; business process modelling and management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Business intelligence and big data applications in e-Commerce have already dominated the world of retail and digital marketing. The digitalization of businesses and interdisciplinary topics such as marketing intelligence, customer profiling and recommendation systems are rapidly evolving areas in which heavy research efforts are produced by academic institutes and intensive developments are driven by strong competition among enterprises.

The current Special Issue focuses on advances and applications of big data and cognitive techniques in e-Commerce. Emphasis is given to methods and developments for intelligent systems, which are aimed at various business goals such as improved effectiveness of digital marketing and optimization of customer experience. Data-driven methods of particular interest are related to advanced consumer profiling and prediction of behavior, intelligent recommendations and automated interactive personalized marketing.

The purpose of the issue is to highlight the link between big data algorithms and human behavior such as personality, user acceptance, reactance to advertising and prediction of hidden desires. The research welcomed in this issue should aim at developments in intelligent systems that focus on the achievement of business goals in e-Commerce, considering user acceptance and impact maximization techniques.

The current literature reflects the rapid progress in solving complex business intelligence problems aimed at higher levels of cognition that more precisely adapt not only to users' explicit needs but also to their hidden desires and feelings. This trend is expected to continue, driven by the increasing availability of huge volumes of e-Commerce data and the strong processing capabilities of big data platforms. This Special Issue will address the challenges which remain towards interdisciplinary approaches that merge the latest findings of business and marketing research with state-of-the-art big data algorithms.

The scope of the Special Issue includes, but is not limited to, the following topics:

  • Business analytics;
  • Intelligent marketing platforms;
  • Data-driven marketing strategy and decision support;
  • Recommender systems;
  • Personalized marketing methods and microtargeting;
  • Machine learning applied to consumer behavior;
  • Deep learning networks in eCommerce;
  • Omni-channel e-Commerce platforms.

Prof. Dr. George Stalidis
Dr. Dimitrios Kardaras
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. Big Data and Cognitive Computing is an international peer-reviewed open access monthly 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 1800 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.

Keywords

  • business intelligence
  • big data in e-commerce
  • marketing intelligence
  • consumer profiling
  • recommendation systems

Published Papers (2 papers)

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Research

23 pages, 2866 KiB  
Article
Exploiting Rating Prediction Certainty for Recommendation Formulation in Collaborative Filtering
by Dionisis Margaris, Kiriakos Sgardelis, Dimitris Spiliotopoulos and Costas Vassilakis
Big Data Cogn. Comput. 2024, 8(6), 53; https://doi.org/10.3390/bdcc8060053 - 27 May 2024
Viewed by 263
Abstract
Collaborative filtering is a popular recommender system (RecSys) method that produces rating prediction values for products by combining the ratings that close users have already given to the same products. Afterwards, the products that achieve the highest prediction values are recommended to the [...] Read more.
Collaborative filtering is a popular recommender system (RecSys) method that produces rating prediction values for products by combining the ratings that close users have already given to the same products. Afterwards, the products that achieve the highest prediction values are recommended to the user. However, as expected, prediction estimation may contain errors, which, in the case of RecSys, will lead to either not recommending a product that the user would actually like (i.e., purchase, watch, or listen) or to recommending a product that the user would not like, with both cases leading to degraded recommendation quality. Especially in the latter case, the RecSys would be deemed unreliable. In this work, we design and develop a recommendation algorithm that considers both the rating prediction values and the prediction confidence, derived from features associated with rating prediction accuracy in collaborative filtering. The presented algorithm is based on the rationale that it is preferable to recommend an item with a slightly lower prediction value, if that prediction seems to be certain and safe, over another that has a higher value but of lower certainty. The proposed algorithm prevents low-confidence rating predictions from being included in recommendations, ensuring the recommendation quality and reliability of the RecSys. Full article
(This article belongs to the Special Issue Business Intelligence and Big Data in E-commerce)
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23 pages, 1966 KiB  
Article
Imagine and Imitate: Cost-Effective Bidding under Partially Observable Price Landscapes
by Xiaotong Luo, Yongjian Chen, Shengda Zhuo, Jie Lu, Ziyang Chen, Lichun Li, Jingyan Tian, Xiaotong Ye and Yin Tang
Big Data Cogn. Comput. 2024, 8(5), 46; https://doi.org/10.3390/bdcc8050046 - 28 Apr 2024
Viewed by 702
Abstract
Real-time bidding has become a major means for online advertisement exchange. The goal of a real-time bidding strategy is to maximize the benefits for stakeholders, e.g., click-through rates or conversion rates. However, in practise, the optimal bidding strategy for real-time bidding is constrained [...] Read more.
Real-time bidding has become a major means for online advertisement exchange. The goal of a real-time bidding strategy is to maximize the benefits for stakeholders, e.g., click-through rates or conversion rates. However, in practise, the optimal bidding strategy for real-time bidding is constrained by at least three aspects: cost-effectiveness, the dynamic nature of market prices, and the issue of missing bidding values. To address these challenges, we propose Imagine and Imitate Bidding (IIBidder), which includes Strategy Imitation and Imagination modules, to generate cost-effective bidding strategies under partially observable price landscapes. Experimental results on the iPinYou and YOYI datasets demonstrate that IIBidder reduces investment costs, optimizes bidding strategies, and improves future market price predictions. Full article
(This article belongs to the Special Issue Business Intelligence and Big Data in E-commerce)
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