OJXPerf

Li, Bolun; Xu, Hao; Zhao, Qi; Su, Pengfei; Chabbi, Milind; Jiao, Shuyin; Liu, Xu

doi:10.1145/3510003.3510083

Cited by 9 publications

(6 citation statements)

References 31 publications

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“…Initially adopted by major marketplaces, these models have since evolved and adapted to augment and streamline the product recommendation processes within individual e-commerce platforms. Efficient e-commerce performance is also crucial for real-time processing and responsiveness, which are essential factors in systems that provide recommendations, especially in dynamic and data-intensive environments [5,6].…”

Section: Literature Reviewmentioning

confidence: 99%

Precision-Driven Product Recommendation Software: Unsupervised Models, Evaluated by GPT-4 LLM for Enhanced Recommender Systems

Roumeliotis,

Tselikas,

Nasiopoulos

2024

Software

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This paper presents a pioneering methodology for refining product recommender systems, introducing a synergistic integration of unsupervised models—K-means clustering, content-based filtering (CBF), and hierarchical clustering—with the cutting-edge GPT-4 large language model (LLM). Its innovation lies in utilizing GPT-4 for model evaluation, harnessing its advanced natural language understanding capabilities to enhance the precision and relevance of product recommendations. A flask-based API simplifies its implementation for e-commerce owners, allowing for the seamless training and evaluation of the models using CSV-formatted product data. The unique aspect of this approach lies in its ability to empower e-commerce with sophisticated unsupervised recommender system algorithms, while the GPT model significantly contributes to refining the semantic context of product features, resulting in a more personalized and effective product recommendation system. The experimental results underscore the superiority of this integrated framework, marking a significant advancement in the field of recommender systems and providing businesses with an efficient and scalable solution to optimize their product recommendations.

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Section: Literature Reviewmentioning

confidence: 99%

Precision-Driven Product Recommendation Software: Unsupervised Models, Evaluated by GPT-4 LLM for Enhanced Recommender Systems

Roumeliotis,

Tselikas,

Nasiopoulos

2024

Software

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“…There are several types of software testing that target different objectives, such as effectiveness, efficiency, user satisfaction, completeness, defect types, etc. For example, two recent works focus on testing and detecting specific memory issues in Android devices [15] and object replication in Java applications [16]. Following the different testing objectives mentioned by Hynninen et al [17], one area that has received significant attention lately is that of testing software to examine if there are security vulnerabilities that can be exploited by an attacker [18].…”

Section: Related Workmentioning

confidence: 99%

A Survey on Factors Preventing the Adoption of Automated Software Testing: A Principal Component Analysis Approach

Murazvu,

Parkinson,

Khan

et al. 2024

Software

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Automated software testing is a crucial yet resource-intensive aspect of software development. This burden on resources affects widespread adoption, with expertise and cost being the primary challenges preventing adoption. This paper focuses on automated testing driven by manually created test cases, acknowledging its advantages while critically analysing its implications across various development stages that are affecting its adoption. Additionally, it analyses the differences in perception between those in nontechnical and technical roles, where nontechnical roles (e.g., management) predominantly strive to reduce costs and delivery time, whereas technical roles are often driven by quality and completeness. This study investigates the difference in attitudes toward automated testing (AtAT), specifically focusing on why it is not adopted. This article presents a survey conducted among software industry professionals that spans various roles to determine common trends and draw conclusions. A two-stage approach is presented, comprising a comprehensive descriptive analysis and the use of Principal Component Analysis. In total, 81 participants received a series of 22 questions, and their responses were compared against job role types and experience levels. In summary, six key findings are presented that cover expertise, time, cost, tools and techniques, utilisation, organisation, and capacity.

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“…-Calculate the average value of the sum of the shortest path lengths of all feature words in the sample vector in the knowledge graph according to Equation (10), called the association distance p(x, y), where m represents the number of feature words with the shortest path not being 0;…”

Section: Vulnerability Classification Algorithm Based On Feature Know...mentioning

confidence: 99%

“…This makes it difficult for them to effectively deal with the large amount of vulnerability information emerging from IoV. Vulnerability classification [10] is essentially a processing method for vulnerability information, which analyzes and categorizes vulnerabilities based on their characteristics, attributes, etc., by choosing appropriate classification algorithms [11]. However, there are multiple types of vulnerabilities in the telematics environment [12][13][14], some of which occur less frequently, resulting in their relatively small sample size and incomplete vulnerability information.…”

Section: Introductionmentioning

confidence: 99%

IoV Vulnerability Classification Algorithm Based on Knowledge Graph

Wang,

Song

et al. 2023

Electronics

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With the rapid development of smart technologies, the Internet of Vehicles (IoV) is revolutionizing transportation and mobility. However, the complexity and interconnectedness of IoV systems lead to a growing number of security incidents caused by vulnerabilities. Current vulnerability classification algorithms often struggle to address the low occurrence frequency and incomplete information associated with IoV vulnerabilities, resulting in decreased precision and recall rates of classifiers. To address these challenges, an effective vulnerability classification algorithm (KG-KNN), is proposed, designed to handle imbalanced sample data. KG-KNN integrates the vulnerability information of IoV and the association relationship between features by constructing a feature knowledge graph to form a complete knowledge system. It adds the correlation relationship between features to the similarity calculation, calculates vulnerability similarity from multiple dimensions, and improves the prediction performance of the classifier. The experimental results show that compared to the k-NearestNeighbor (KNN), Support Vector Machine (SVM), Deep Nueral Network (DNN) and TFI-DNN classification algorithms, KG-KNN can effectively deal with imbalanced sample data and has different degrees of improvement in precision, recall, and the F1 score.

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OJXPerf

Cited by 9 publications

References 31 publications

Precision-Driven Product Recommendation Software: Unsupervised Models, Evaluated by GPT-4 LLM for Enhanced Recommender Systems

Precision-Driven Product Recommendation Software: Unsupervised Models, Evaluated by GPT-4 LLM for Enhanced Recommender Systems

A Survey on Factors Preventing the Adoption of Automated Software Testing: A Principal Component Analysis Approach

IoV Vulnerability Classification Algorithm Based on Knowledge Graph

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