Efficient Personalization in E-Commerce: Leveraging Universal Customer Representations with Embeddings
E-commerce has grown into a billion-dollar industry in recent times with an ever-increasing number of individuals using it regularly. Thus, e-commerce companies can gather interaction data from their customers and analyze it to create focused and personalized marketing campaigns. For large companies...
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| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Journal of Theoretical and Applied Electronic Commerce Research |
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| Online Access: | https://www.mdpi.com/0718-1876/20/1/12 |
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| author | Miguel Alves Gomes Philipp Meisen Tobias Meisen |
| author_facet | Miguel Alves Gomes Philipp Meisen Tobias Meisen |
| author_sort | Miguel Alves Gomes |
| collection | DOAJ |
| description | E-commerce has grown into a billion-dollar industry in recent times with an ever-increasing number of individuals using it regularly. Thus, e-commerce companies can gather interaction data from their customers and analyze it to create focused and personalized marketing campaigns. For large companies, it is possible to tap into these data for personalization using deep learning-based methods that require enormous computing resources. Small companies, on the other hand, cannot afford this. Furthermore, this level of tailor-made addressing necessitates an accurate customer representation. Nevertheless, the exploration of universal representations applicable across various tasks has been limited despite the advantages they offer. We propose a universal customer representation learned only from customer interaction data. We demonstrate that self-supervised trained embeddings of the customer interaction context are a suitable universal customer representation for various e-commerce tasks. To demonstrate the effectiveness of our approach, we conducted experiments comparing four different state-of-the-art approaches and their capabilities in different prediction tasks. Not only do we show that our method outperforms others in most cases, but it also meets other important criteria for real-world applications. It is particularly important to emphasize that our approach does not require a significant amount of resources, and furthermore, is data protection compliant by not using personal information. |
| format | Article |
| id | doaj-art-a80ade142f764d13ab31e645cb81f4b0 |
| institution | OA Journals |
| issn | 0718-1876 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Theoretical and Applied Electronic Commerce Research |
| spelling | doaj-art-a80ade142f764d13ab31e645cb81f4b02025-08-20T01:48:52ZengMDPI AGJournal of Theoretical and Applied Electronic Commerce Research0718-18762025-01-012011210.3390/jtaer20010012Efficient Personalization in E-Commerce: Leveraging Universal Customer Representations with EmbeddingsMiguel Alves Gomes0Philipp Meisen1Tobias Meisen2Institute for Technologies and Management of Digital Transformation, University of Wuppertal, 42119 Wuppertal, GermanyBreinify Inc., San Francisco, CA 94105, USAInstitute for Technologies and Management of Digital Transformation, University of Wuppertal, 42119 Wuppertal, GermanyE-commerce has grown into a billion-dollar industry in recent times with an ever-increasing number of individuals using it regularly. Thus, e-commerce companies can gather interaction data from their customers and analyze it to create focused and personalized marketing campaigns. For large companies, it is possible to tap into these data for personalization using deep learning-based methods that require enormous computing resources. Small companies, on the other hand, cannot afford this. Furthermore, this level of tailor-made addressing necessitates an accurate customer representation. Nevertheless, the exploration of universal representations applicable across various tasks has been limited despite the advantages they offer. We propose a universal customer representation learned only from customer interaction data. We demonstrate that self-supervised trained embeddings of the customer interaction context are a suitable universal customer representation for various e-commerce tasks. To demonstrate the effectiveness of our approach, we conducted experiments comparing four different state-of-the-art approaches and their capabilities in different prediction tasks. Not only do we show that our method outperforms others in most cases, but it also meets other important criteria for real-world applications. It is particularly important to emphasize that our approach does not require a significant amount of resources, and furthermore, is data protection compliant by not using personal information.https://www.mdpi.com/0718-1876/20/1/12e-commercecustomer behavioruniversal customer representationbehavior predictionembeddingneural networks |
| spellingShingle | Miguel Alves Gomes Philipp Meisen Tobias Meisen Efficient Personalization in E-Commerce: Leveraging Universal Customer Representations with Embeddings Journal of Theoretical and Applied Electronic Commerce Research e-commerce customer behavior universal customer representation behavior prediction embedding neural networks |
| title | Efficient Personalization in E-Commerce: Leveraging Universal Customer Representations with Embeddings |
| title_full | Efficient Personalization in E-Commerce: Leveraging Universal Customer Representations with Embeddings |
| title_fullStr | Efficient Personalization in E-Commerce: Leveraging Universal Customer Representations with Embeddings |
| title_full_unstemmed | Efficient Personalization in E-Commerce: Leveraging Universal Customer Representations with Embeddings |
| title_short | Efficient Personalization in E-Commerce: Leveraging Universal Customer Representations with Embeddings |
| title_sort | efficient personalization in e commerce leveraging universal customer representations with embeddings |
| topic | e-commerce customer behavior universal customer representation behavior prediction embedding neural networks |
| url | https://www.mdpi.com/0718-1876/20/1/12 |
| work_keys_str_mv | AT miguelalvesgomes efficientpersonalizationinecommerceleveraginguniversalcustomerrepresentationswithembeddings AT philippmeisen efficientpersonalizationinecommerceleveraginguniversalcustomerrepresentationswithembeddings AT tobiasmeisen efficientpersonalizationinecommerceleveraginguniversalcustomerrepresentationswithembeddings |