An Improved Decision Tree Model for Forecasting Consumer Decision in a Medium Groceries Store

Rahmatillah, Intan; Astuty, Eriana; Sudirman, Ivan Diryana

doi:10.1109/iciis58898.2023.10253592

Cited by 4 publications

(3 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Up-selling strategies, designed to promote higher-value products, rely on sophisticated algorithms like Decision Trees. These trees analyze customer behavior, pinpointing factors leading to premium purchases [20]. Concurrently, ranking models such as RankNet or LambdaMART contribute by predicting and ranking products based on their revenue potential, ensuring personalized recommendations that resonate with customer desires [21].…”

Section: Product Recommendation Models and Algorithmsmentioning

confidence: 99%

“…Concurrently, ranking models such as RankNet or LambdaMART contribute by predicting and ranking products based on their revenue potential, ensuring personalized recommendations that resonate with customer desires [21]. The synergy between Decision Trees and ranking models equips businesses to optimize up-selling initiatives and maximize revenue opportunities [20].…”

Section: Product Recommendation Models and Algorithmsmentioning

confidence: 99%

See 1 more Smart Citation

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

Roumeliotis,

Tselikas,

Nasiopoulos

2024

Software

View full text Add to dashboard Cite

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.

show abstract

Section: Product Recommendation Models and Algorithmsmentioning

confidence: 99%

Section: Product Recommendation Models and Algorithmsmentioning

confidence: 99%

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

Roumeliotis,

Tselikas,

Nasiopoulos

2024

Software

View full text Add to dashboard Cite

show abstract

“…A decision tree (DT) is an effective classification algorithm proposed by Breiman et al [5]. It has been utilized in diverse applications such as handoffs in Internet of Vehicles [6], screen content coding [7], human activity classification [8], wafer map failure pattern recognition [9], forecasting consumer decision [10], and selecting pet adopters [11]. A DT can be implemented either in software or in hardware, and hardware implementation has recently drawn considerable attention [12,13].…”

Section: Introductionmentioning

confidence: 99%

Fast and Energy-Efficient Oblique Decision Tree Implementation with Potential Error Detection

Lim

2024

Electronics

View full text Add to dashboard Cite

In the contemporary landscape, with the proliferation of cyber-physical systems and the Internet of Things, intelligent embedded systems have become ubiquitous. These systems derive their intelligence from machine learning algorithms that are integrated within them. Among many machine learning algorithms, decision trees are often favored for implementation in such systems due to their simplicity and commendable classification performance. In this regard, we have proposed the efficient implementations of a fixed-point decision tree tailored for embedded systems. The proposed approach begins by identifying an input vector that might be classified differently by a fixed-point decision tree than by a floating-point decision tree. Upon identification, an error flag is activated, signaling a potential misclassification. This flag serves to bypass or disable the subsequent classification procedures for the identified input vector, thereby conserving energy and reducing classification latency. Subsequently, the input vector is alternatively classified based on class probabilities gathered during the training phase. In comparison with traditional fixed-point implementations, our proposed approach is proven to be 23.9% faster in terms of classification speed, consuming 11.5% less energy without compromising classification accuracy. The proposed implementation, if adopted in a smart embedded device, can provide a more responsive service to its users as well as longer battery life.

show abstract

A Real-Time System Status Evaluation Method for Passive UHF RFID Robots in Dynamic Scenarios

Wang,

Du,

Qin

et al. 2024

Electronics

View full text Add to dashboard Cite

In dynamic scenarios, the status of a Radio Frequency Identification (RFID) system fluctuates with environmental changes. The key to improving system efficiency lies in the real-time monitoring and evaluation of the system status, along with adaptive adjustments to the system parameters and read algorithms. This paper focuses on the status changes in RFID systems in dynamic scenarios, aiming to enhance system robustness and reading performance, ensuring high link quality, reasonable resource scheduling, and real-time status evaluation under varying conditions. This paper comprehensively considers the system parameter settings in dynamic scenarios, integrating the interaction model between readers and tags. The system’s real-time status is evaluated from both the physical layer and the Medium Access Control (MAC) layer perspectives. For the physical layer, a link quality evaluation model based on Uniform Manifold Approximation and Projection (UMAP) and K-Means clustering is proposed from the link quality. For the MAC layer, a multi-criteria decision-making evaluation model based on combined weighting and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is proposed, which comprehensively considers both subjective and objective factors, utilizing the TOPSIS algorithm for an accurate evaluation of the MAC layer system status. For the RFID system, this paper proposes a real-time status evaluation model based on the Classification and Regression Tree (CART), which synthesizes the evaluation results of the physical layer and MAC layer. Finally, engineering tests and verification were conducted on the RFID robot system in mobile scenarios. The results showed that the clustering average silhouette coefficient of the physical layer link quality evaluation model based on K-Means was 0.70184, indicating a relatively good clustering effect. The system status evaluation model of the MAC layer, based on the combined weighting-TOPSIS method, demonstrated good flexibility and generalization. The real-time status evaluation model of the RFID system, based on CART, achieved a classification accuracy of 98.3%, with an algorithm runtime of 0.003 s. Compared with other algorithms, it had a higher classification accuracy and shorter runtime, making it well suited for the real-time evaluation of the RFID robot system’s status in dynamic scenarios.

show abstract

An Improved Decision Tree Model for Forecasting Consumer Decision in a Medium Groceries Store

Cited by 4 publications

References 16 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

Fast and Energy-Efficient Oblique Decision Tree Implementation with Potential Error Detection

A Real-Time System Status Evaluation Method for Passive UHF RFID Robots in Dynamic Scenarios

Contact Info

Product

Resources

About