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Shopping transactions in digital retailing platforms enable retailers to understand customers' needs for providing personalized experiences. Researchers started modeling transaction data through neural network embedding, which enables unsupervised learning of contextual similarities between attributes in shopping transactions. However, every study brings different approaches for embedding customer's transactions, and clear preprocessing guidelines are missing. This paper reviews the recentdoi:10.1145/3386164.3389092 dblp:conf/iscsic/CirqueiraHB19 fatcat:3bfenjcfxzcsnotrmbiceafg3e