E-commerce de café natural basado en un sistema de recomendación

• Autor: Moquillaza Vizarreta, Iván David
• Formato: tesis de grado
• Fecha de Publicación: 2023
• Institución: Universidad de Lima
• Repositorio: ULIMA-Institucional
• Lenguaje: español
• OAI Identifier: oai:repositorio.ulima.edu.pe:20.500.12724/19146
• Enlace del recurso: https://hdl.handle.net/20.500.12724/19146
• Nivel de acceso: acceso abierto
• Materia: Café; Comercio electrónico; Sistemas de recomendación (Filtrado de información); Coffee; Electronic commerce; Recommender systems (Information filtering); https://purl.org/pe-repo/ocde/ford#2.02.04

Nowadays, coffee is one of the main agricultural products that Peru exports to the main international markets; despite this, national consumption is incipient and in preference to imported coffee. In recent years, there has been an increase in the consumption of natural coffee due to specialty coffee shops and the high-value formats that they offer. However, in the field work, it was identified that consumers seek to take home the experience of these cafeterias, have options to choose the coffee that best suits their tastes and avoid buying standard products - dark roast coffee and medium grinding - that are sold in supermarkets. For this reason, the present work seeks to revalue national coffee, contributing to the dissemination of Peruvian coffee varieties, the promotion of its consumption, the protection of coffee farms and the strengthening of communities; in order to avoid the eradication of coffee plantations and their conversion to coca leaf plantations. In this sense, technology will be the pillar on which the business model will be built, which seeks to provide the customer the experience of a specialty cafeteria and offer products in a personalized and timely way. For this, the Design Thinking methodology was used, to understand the client's needs and prototype possible solutions; Likewise, the Scrum methodology and the Rapid Miner tool were used to develop the Operating Plan, Financial Plan, Recommendation System and Monitoring Dashboard. The use of the FP-Growth algorithm for the creation of the rules of the recommendation system is highlighted. Finally, the use of technology in the coffee ecosystem is expected to boost its consumption and create a new business mode.