TY - JOUR
T1 - Use of latent factors and consumption patterns for the construction of a recommender system
AU - Alatrista-Salas, Hugo
AU - Hoyos, Isaias
AU - Luna, Ana
AU - Nunez-Del-Prado, Miguel
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/1/1
Y1 - 2019/1/1
N2 - In recent years, the recommender systems have become essential tools for companies to offer their products in a personalized way and to improve the user experience. The primary objective of these systems is to propose products or services to the user, according to specific criteria, such as their interests, their preferences, the place where they work or where they live. The problem arises when the system recommends products from an establishment to users who never visited that establishment. Besides, it is known that the order in which users purchase certain products or services can impact on the recommendation. To deal with these two problems, we propose a process that combines two widely used models: Latent factors and matrix factorization. Also, to include temporality in our results, we use the Sequitur algorithm. In order to test our proposal, we have used a database with approximately 65 million banking transactions. The results obtained show the efficiency of our proposal in terms of average consumption ticket increase.
AB - In recent years, the recommender systems have become essential tools for companies to offer their products in a personalized way and to improve the user experience. The primary objective of these systems is to propose products or services to the user, according to specific criteria, such as their interests, their preferences, the place where they work or where they live. The problem arises when the system recommends products from an establishment to users who never visited that establishment. Besides, it is known that the order in which users purchase certain products or services can impact on the recommendation. To deal with these two problems, we propose a process that combines two widely used models: Latent factors and matrix factorization. Also, to include temporality in our results, we use the Sequitur algorithm. In order to test our proposal, we have used a database with approximately 65 million banking transactions. The results obtained show the efficiency of our proposal in terms of average consumption ticket increase.
KW - Consumption Patterns
KW - Latent Factors
KW - Matrix factorization
KW - Recommender system
KW - Consumption Patterns
KW - Latent Factors
KW - Matrix factorization
KW - Recommender system
UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85072261394&origin=inward
UR - http://www.scopus.com/inward/record.url?scp=85072261394&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/dfeb2760-5e7b-3d62-83dd-8ac36a459a54/
U2 - 10.1109/TLA.2019.8826703
DO - 10.1109/TLA.2019.8826703
M3 - Article in a journal
SN - 1548-0992
VL - 17
SP - 119
EP - 126
JO - IEEE Latin America Transactions
JF - IEEE Latin America Transactions
IS - 1
M1 - 8826703
ER -