Steven Simske

Colorado State University
Country flagUnited States

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Articles (19)

Year:

2025

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2025

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2024

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2024

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2024

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2024

This study proposes a framework for a systems engineering-based approach to context-aware personalization, which is applied to e-commerce through the understanding and modeling of user behavior from their interactions with sales channels and media. The framework is practical and built on systems engineering principles. It combines three conceptual components to produce signals that provide content relevant to the users based on their behavior, thus enhancing their experience. These components are the ‘recognition and knowledge’ of the users and their behavior (persona); the awareness of users’ current contexts; and the comprehension of their situation and projection of their future status (intent prediction). The persona generator is implemented by leveraging an unsupervised machine learning algorithm to assign users into cohorts and learn cohort behavior while preserving their privacy in an ethical framework. The component of the users’ current context is fulfilled as a microservice that adopts novel e-commerce data interpretations. The best result of 97.3% accuracy for the intent prediction component was obtained by tokenizing categorical features with a pre-trained BERT (bidirectional encoder representations from transformers) model and passing these, as the contextual embedding input, to an LSTM (long short-term memory) neural network. Paired cohort-directed prescriptive action is generated from learned behavior as a recommended alternative to users’ shopping steps. The practical implementation of this e-commerce personalization framework is demonstrated in this study through the empirical evaluation of experimental results.

Year:

2023

Blockchain technology has emerged as a tool with the potential to enhance transparency, trust, security, and decentralization in supply chain management (SCM). This study presents a comprehensive review of the interplay between blockchain technology and SCM. By analyzing an extensive dataset of 943 articles, our exploration utilizes the Latent Dirichlet Allocation (LDA) method to delve deep into the thematic structure of the discourse. This investigation revealed ten central topics ranging from blockchain’s transformative role in supply chain finance and e-commerce operations to its application in specialized areas, such as the halal food supply chain and humanitarian contexts. Particularly pronounced were discussions on the challenges and transformations of blockchain integration in supply chains and its impact on pricing strategies and decision-making. Visualization tools, including PyLDAvis, further illuminated the interconnectedness of these themes, highlighting the intertwined nature of blockchain adoption challenges with aspects such as traceability and pricing. Despite the breadth of topics covered, the paper acknowledges its limitations due to the fast-evolving nature of blockchain developments during and after our analysis period. Ultimately, this review provides a holistic academic snapshot, emphasizing both well-developed and nascent research areas and guiding future research in the evolving domain of blockchain in SCM.

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2023

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2023

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2023

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2023

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2023

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2023

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2023

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2023

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2023

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2022

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2022

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2022

Collaborators (7)

John G. Breslin

University of Galway

IRELAND
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John G. Keogh

McGill University

CANADA
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Jason C. Quinn

Colorado State University

UNITED STATES
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WAYNE MARTINDALE

University of Lincoln

UNITED KINGDOM
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subhasis thakur

National University of Ireland Galway

IRELAND
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Edwin Chong

Colorado State University

UNITED STATES
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Olurotimi Oguntola

Colorado State University

UNITED STATES
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