Market basket analysis by solving the inverse Ising problem: Discovering pairwise interaction strengths among products
Valle
M
A
author
Ruz
G
A
author
Rica
S
author
2019
English
Large datasets containing the purchasing information of thousands of consumers are difficult to analyze because the possible number of different combinations of products is huge. Thus, market baskets analysis to obtain useful information and find interesting pattern of buying behavior could be a daunting task. Based on the maximum entropy principle, we build a probabilistic model that explains the probability of occurrence of market baskets which is equivalent to Ising models. This type of model allows us to understand and to explore the functional interactions among products that make up the market offer. Additionally, the parameters of the model inferred using Boltzmann learning, allow us to suggest that the buying behavior is very similar to the spin-glass physical system. Moreover, we show that the resulting parameters of the model could be useful to describe the hierarchical structure of the system which leads to interesting information about the different market baskets. (C) 2019 Elsevier B.V. All rights reserved.
Inverse Ising problem
Boltzmann machine
Transactional data base
Pairwise interaction
Minimum spanning tree
Purchase pattern
WOS:000476966100004
exported from refbase (http://ficpubs.uai.cl/show.php?record=1022), last updated on Wed, 14 Aug 2019 22:04:45 +0000
text
http://ficpubs.uai.cl/files/1002_Valle_etal2019.pdf
10.1016/j.physa.2019.03.001
Valle_etal2019
Physica A-Statistical Mechanics And Its Applications
Physica A
2019
Elsevier
continuing
periodical
academic journal
524
36
44
0378-4371