{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T14:51:55Z","timestamp":1773931915240,"version":"3.50.1"},"reference-count":54,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,8,17]],"date-time":"2023-08-17T00:00:00Z","timestamp":1692230400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Open Access Publication Fund of the University of Wuppertal"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JTAER"],"abstract":"<jats:p>Real-time customer purchase prediction tries to predict which products a customer will buy next. Depending on the approach used, this involves using data such as the customer\u2019s past purchases, his or her search queries, the time spent on a product page, the customer\u2019s age and gender, and other demographic information. These predictions are then used to generate personalized recommendations and offers for the customer. A variety of approaches already exist for real-time customer purchase prediction. However, these typically require expertise to create customer representations. Recently, embedding-based approaches have shown that customer representations can be effectively learned. In this regard, however, the current state-of-the-art does not consider activity time. In this work, we propose an extended embedding approach to represent the customer behavior of a session for both known and unknown customers by including the activity time. We train a long short-term memory with our representation. We show with empirical experiments on three different real-world datasets that encoding activity time into the embedding increases the performance of the prediction and outperforms the current approaches used.<\/jats:p>","DOI":"10.3390\/jtaer18030070","type":"journal-article","created":{"date-parts":[[2023,8,17]],"date-time":"2023-08-17T10:49:00Z","timestamp":1692269340000},"page":"1404-1418","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["TEE: Real-Time Purchase Prediction Using Time Extended Embeddings for Representing Customer Behavior"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3664-0360","authenticated-orcid":false,"given":"Miguel","family":"Alves Gomes","sequence":"first","affiliation":[{"name":"Institute for Technologies and Management of Digital Transformation, University of Wuppertal, 42119 Wuppertal, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4052-2658","authenticated-orcid":false,"given":"Mark","family":"W\u00f6nkhaus","sequence":"additional","affiliation":[{"name":"Institute for Technologies and Management of Digital Transformation, University of Wuppertal, 42119 Wuppertal, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8024-3074","authenticated-orcid":false,"given":"Philipp","family":"Meisen","sequence":"additional","affiliation":[{"name":"Breinify Inc., San Francisco, CA 94105, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1969-559X","authenticated-orcid":false,"given":"Tobias","family":"Meisen","sequence":"additional","affiliation":[{"name":"Institute for Technologies and Management of Digital Transformation, University of Wuppertal, 42119 Wuppertal, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"39","DOI":"10.4018\/JGIM.2020010103","article-title":"E-service quality and trust on customer\u2019s patronage intention: Moderation effect of adoption of advanced technologies","volume":"28","author":"Rahman","year":"2020","journal-title":"J. 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