Machine learning based recommender system for e-commerce

Abstract

<span>Nowadays, e-commerce is becoming an essential part of business for many reasons, including the simplicity, availability, richness and diversity of products and services, flexibility of payment methods and the convenience of shopping remotely without losing time. These benefits have greatly optimized the lives of users, especially with the technological development of mobile devices and the availability of the Internet anytime and anywhere. Because of their direct impact on the revenue of e-commerce companies, recommender systems are considered a must in this field. Recommender systems detect items that match the customer's needs based on the customer's previous actions and make them appear in an interesting way. Such a customized experience helps to increase customer engagement and purchase rates as the suggested items are tailored to the customer's interests. Therefore, perfecting recommendation systems that allow for more personalized and accurate item recommendations is a major challenge in the e-marketing world. In our study, we succeeded in developing an algorithm to suggest personal recommendations to customers using association rules via the Frequent-Pattern-Growth algorithm. Our technique generated good results with a high average probability of purchasing the next product suggested by the recommendation system.</span>

A Time-Sensitive Graph Neural Network for Session-Based New Item Recommendation

“…Recommender systems significantly impact people's daily lives; they filter information for people and recommend things that people may be interested in [1]. Recommendation systems widely exist in different fields, such as e-commerce [2], job markets [3], and streaming media [4]. A key scenario in recommendation systems is session-based recommendations.…”

“…With this graph modeling technique, the model considers the temporal sequence of item interactions, ensuring that the appearance of each item is taken into account, and reflects user interactions and preferences over time. (2) To tackle the challenge of sparse sessions, we incorporate a graph augmentation technique into the session graph. This technique involves altering the original graphs, allowing the TSGNN to generate a multitude of augmented graphs.…”

A Smart Model to enhance Customer retention in E-commerce

“…Today, e-commerce is essential for various reasons, offering simplicity, diverse products, flexible payment methods, and the ease of remote shopping. Technological advancements in mobile devices and widespread Internet access have greatly improved user experiences, directly impacting the revenue of ecommerce companies [8]. E-commerce platforms offer crucial insights into modern consumer behavior, preferences, and social interactions.…”

Modelo de aceptación tecnológica en los canales digitales para la adquisición de productos agrícolas frescos

“…El uso de las tecnologías de la información y la comunicación (TIC) en la gestión organizacional ha generado importantes cambios en el entorno empresarial (Hovhannisyan y Chobanya, 2018;Steel, 2021), como la migración de la comercialización de bienes y servicios desde un entorno físico hacia uno digital (Higueras Redecillas et al, 2016;Pesántez-Calva et al, 2020) y la adaptación a mercados altamente competitivos (Maita Guédez, 2019). El comercio electrónico (e-commerce) se establece como una herramienta que otorga múltiples beneficios tanto para el productor y como para el consumidor (Loukili et al, 2023), como lo son: la reducción de costos en las transacciones (Hasan y Niyogi, 2020), transporte y almacenamiento, la rapidez en el escalonamiento de los negocios, la comparación de precios y calidades por parte del consumidor, la eliminación del intermediario, la mayor agilidad en los procesos operacionales y el traspaso de las barreras geográficas (González García, 2020;Oubiña Barbolla, 2020;Pesántez-Calva et al, 2020;Silva Murillo, 2009).…”