A Social-aware and Mobile Computing-based E-Commerce Product Recommendation System

Xu, Qin; Wang, Jun

doi:10.1155/2022/9501246

Cited by 8 publications

(5 citation statements)

References 23 publications

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“…Massive data is stored in Internet databases or other data storage devices in various fields related to our daily life, such as business, social sciences, engineering, and medical treatment. The surge of available data is the result of the rapid development of software and hardware in a highly information society, and the data collection capacity and storage devices are increasing [ 12 ]. Generally, the pricing methods of e-commerce agricultural products marketing projects mainly include market comparison pricing method, cost pricing method, market competition pricing method, and future income pricing method.…”

Section: Methodsmentioning

confidence: 99%

Precise Marketing of E-Commerce Products Based on KNN Algorithm

Zou

2022

Computational Intelligence and Neuroscience

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In order to better understand the purchase decision-making process of consumers, this paper makes an in-depth study on the precision marketing of e-commerce products on the basis of KNN algorithm. Through data mining, classic KNN algorithm, BPNN algorithm, and other methods, this paper takes the price and purchase intention of e-commerce agricultural products as an example. Based on the classic nearest neighbor algorithm, binomial function is combined with Euclidean distance formula when calculating the nearest neighbor through similarity. The particle swarm optimization algorithm is used to optimize the binomial function coefficient and the K value of the nearest neighbor algorithm, and the results of the best prediction model for the prediction application of e-commerce agricultural product price and purchase intention are established. Both pricing strategies and promotion strategies will weaken the compromise effect of consumers when they choose e-commerce agricultural products. After studying the calculation method of the KNN algorithm, it not only correctly predicts the price of e-commerce agricultural products but also makes a corresponding prediction and analysis of consumers’ purchase intention of e-commerce agricultural products, with the highest accuracy of 94.2%. At the same time, in the future precision marketing process, e-commerce agricultural products enterprises use data technology to achieve precision marketing, which effectively changes the shortcomings of traditional marketing and improves the product marketing effect and economic benefits.

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Section: Methodsmentioning

confidence: 99%

Precise Marketing of E-Commerce Products Based on KNN Algorithm

Zou

2022

Computational Intelligence and Neuroscience

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“…The overall recall performance of location-based CNN was 8.14%, which is more than two percentage points greater than the standard approach. Xu and Wang [25], this research proposes the mobile computational working method. An online store administration system, complete with its 50,000 products and 2,000 customers, was selected as the evaluation's test subject.…”

Section: Literature Surveymentioning

confidence: 99%

Dual-blend insight recommendation system for e-commerce recommendations and enhance personalization

Silvester,

Kurain

2024

IJEECS

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E-commerce, short for electronic commerce, refers to the buying and selling of goods and services over the internet. This digital transaction model has revolutionized the way businesses operate and consumers shop. In response to the burgeoning complexity of e-commerce datasets, this work addresses the need for advanced recommendation systems. This work introduces the dual-blend insight recommendation system (DIRS) model for personalized e-commerce recommendation system. The DIRS model involves dataset loading, preprocessing, and feature extraction, enabling training with recurrent neural network (RNN) and Bayesian personalized ranking (BPR) models. Recommendations are generated based on user-defined functions, i.e., location and session, and evaluation metrics such as hit rate (HR) and mean reciprocal rate (MRR) highlight DIRS’s superior performance. The model is evaluated using the Tmall dataset. Results reveal DIRS consistently outperforms alternative algorithms, showcasing its effectiveness in 10k and 20k recommendation sets. This study provides valuable insights into optimizing e-commerce recommendations, emphasizing DIRS as a powerful model for enhancing user experience and engagement.

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“…The goal of CF recommendation algorithms is to score recommended or predicted items based on user feedback from previous interaction items (Xu & Wang, 2022). By analyzing user feedback information, the most similar items and user groups are screened from the website.…”

Section: Introductionmentioning

confidence: 99%

Personalized Recommendation Method of E-Commerce Products Based on In-Depth User Interest Portraits

Li,

Bao

2023

International Journal of Information Technology and Web Engineering

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In dynamic e-commerce environments, researchers strive to understand users' interests and behaviors to enhance personalized product recommendations. Traditional collaborative filtering (CF) algorithms have encountered computational challenges such as similarity errors and user rating habits. This research addresses these issues by emphasizing user profiling techniques. This article proposes an innovative user profile updating technique that explores the key components of user profile (basic information, behavior, and domain knowledge). An enhanced kernel fuzzy mean clustering algorithm constructs a dynamic user portrait based on domain knowledge mapping. This dynamic portrait is combined with e-commerce personalized recommendation to improve the accuracy of inferring user interests, thus facilitating accurate recommendation on the platform. The method proposed in this article greatly improves the overall performance and provides strong support for developing smarter and more personalized e-commerce product recommendation systems.

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A Social-aware and Mobile Computing-based E-Commerce Product Recommendation System

Cited by 8 publications

References 23 publications

Precise Marketing of E-Commerce Products Based on KNN Algorithm

Precise Marketing of E-Commerce Products Based on KNN Algorithm

Dual-blend insight recommendation system for e-commerce recommendations and enhance personalization

Personalized Recommendation Method of E-Commerce Products Based on In-Depth User Interest Portraits

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