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首页> 外文期刊>Physica, A. Statistical mechanics and its applications >Market basket analysis by solving the inverse Ising problem: Discovering pairwise interaction strengths among products
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Market basket analysis by solving the inverse Ising problem: Discovering pairwise interaction strengths among products

机译：通过解决逆表现问题的市场篮分析：在产品中发现成对相互作用强度

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摘要

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.
机译：包含成千上万消费者的购买信息的大型数据集难以分析，因为产品的不同组合数量是巨大的。因此，市场篮子分析以获取有用的信息并找到有趣的购买行为模式可能是一个艰巨的任务。基于最大熵原理，我们构建了一个概率模型，解释了市场篮子的发生概率，其等同于ising模型。这种类型的模型使我们能够理解并探索构成市场优惠的产品之间的功能互动。此外，使用Boltzmann学习推断的模型的参数允许我们建议购买行为与旋转玻璃物理系统非常相似。此外，我们表明模型的所得到的参数可用于描述系统的分层结构，这导致有关不同市场篮的有趣信息。 （c）2019 Elsevier B.v.保留所有权利。

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