Use of latent factors and consumption patterns for the construction of a recommender system

Hugo Alatrista-Salas, Isaias Hoyos, Ana Luna, Miguel Nunez-Del-Prado

Research output: Contribution to journalArticle in a journalpeer-review


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.
Original languageEnglish
Article number8826703
Pages (from-to)119-126
Number of pages8
JournalIEEE Latin America Transactions
Issue number1
StatePublished - 1 Jan 2019

Bibliographical note

Publisher Copyright:
© 2019 IEEE.


  • Consumption Patterns
  • Latent Factors
  • Matrix factorization
  • Recommender system


Dive into the research topics of 'Use of latent factors and consumption patterns for the construction of a recommender system'. Together they form a unique fingerprint.

Cite this