Academic literature on the topic 'E-commerce Personalization'

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Journal articles on the topic "E-commerce Personalization"

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Vavliakis, Konstantinos N., George Katsikopoulos, and Andreas L. Symeonidis. "E-commerce Personalization with Elasticsearch." Procedia Computer Science 151 (2019): 1128–33. http://dx.doi.org/10.1016/j.procs.2019.04.160.

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Tarmizi and Lidiana. "Enhancing User Experience in E-commerce through Personalization Algorithms A Study on Information System Design." Journal Informatic, Education and Management (JIEM) 6, no. 1 (2024): 24–28. http://dx.doi.org/10.61992/jiem.v6i1.59.

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This research aims to improve user experience in e-commerce through the use of personalization algorithms in information system design. The research methods used involve literature analysis, prototype development, and user testing. Literature analysis was conducted to understand the concept of personalization, relevant algorithms, and factors influencing user experience in e-commerce. Based on this understanding, a prototype e-commerce information system with personalization features was implemented. User testing is carried out to collect data about user experiences before and after implementi
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Thakur, Dr Kachan. "Emotion-Driven Personalization in E-Commerce." International Scientific Journal of Engineering and Management 04, no. 05 (2025): 1–7. https://doi.org/10.55041/isjem03364.

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Abstract: By incorporating emotional intelligence into online shopping experiences, emotion-driven personalization in e-commerce is a huge change in how companies interact with their customers. Personalization methods that have been used for a long time mostly use behavioral data, like browsing history, purchase patterns, and demographic data. But they often don't take into account the complex emotions that affect how people make decisions. With technologies like AI, machine learning, facial recognition, sentiment analysis, and biometric feedback, emotion-driven personalization tries to close
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Mariia, Bulycheva. "Personalization in E-Commerce: Optimizing Recommendations for Multimodal Content." American Journal of Applied Sciences 07, no. 05 (2025): 57–65. https://doi.org/10.37547/tajas/volume07issue05-06.

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This article examines modern approaches to the personalization of multimodal content in e-commerce, driven by the growing complexity of user requests and the evident need to adapt recommendations to diverse data formats and content modality. The relevance of this topic is underscored by the increasing volume of information associated with the article, including text, images, video, and audio, which necessitates the application of specialized methods for precise customization and improved personalization. The purpose of the study is to develop original proposals for optimizing recommendation al
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Mandagie, Wenny Candra, and Robert Kristaung. "The power of AI personalization: Mediated moderation in social & e-commerce." Jurnal Manajemen dan Pemasaran Jasa 18, no. 1 (2025): 35–58. https://doi.org/10.25105/v18i1.21587.

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This study investigates the impact of AI-driven personalization on key customer outcomes within Indonesia's e-commerce landscape. It examines how personalization influences customer engagement, trust, perceived relevance, customer experience, and purchase frequency. While prior studies have extensively explored AI personalization, limited research has examined its mediated moderation effects on customer behavior in emerging e-commerce markets, particularly in Indonesia. This study addresses the gap by analyzing the moderating effect of education level and the mediating role of perceived releva
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Schubert, Petra, and Uwe Leimstoll. "Personalization of E-Commerce Applications in SMEs." Journal of Electronic Commerce in Organizations 2, no. 3 (2004): 21–39. http://dx.doi.org/10.4018/jeco.2004070102.

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Jinyoung Hwang. "E-commerce and Consumer Behavior." GSC Advanced Research and Reviews 19, no. 3 (2024): 361–71. http://dx.doi.org/10.30574/gscarr.2024.19.3.0139.

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The research investigates consumer behavior within e-commerce, focusing on the factors influencing online purchasing decisions and strategies to enhance customer engagement and loyalty. The study explores psychological, social, and technological elements that shape online behaviors, including personalization, trust-building mechanisms, and user experience optimization. Employing a mixed-methods approach, it examines how e-commerce businesses leverage data analytics, marketing strategies, and customer feedback to adapt to evolving consumer preferences. Special attention is given to the impact o
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Maduwinarti, Ayun, Nugraha Kusbianto, and I. G. N. Andhika Mahendra. "The influence of customer personalization on customer satisfaction, engagement, and loyalty Indonesian E-commerce in the digital age." International Journal of Innovative Research and Scientific Studies 8, no. 3 (2025): 1904–14. https://doi.org/10.53894/ijirss.v8i3.6900.

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In the rapidly evolving landscape of Indonesian e-commerce, this research explores the intricate dynamics between customer personalization and its impact on customer loyalty. Positioned within a burgeoning market projected to exceed USD 95 billion by 2025, the study addresses the competitive challenges faced by e-commerce enterprises in retaining customers. Leveraging personalized content and interactions, the research delves into the mediating roles of customer engagement and customer satisfaction in shaping customer loyalty. Employing a quantitative approach, the research collected data thro
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Galuh Aninditiyah and Ayu Miranti Kusumaningrum. "Personalisasi Produk E-Commerce dengan Kecerdasan Buatan untuk Meningkatkan Loyalitas Pelanggan." E-BISNIS: JURNAL ILMIAH EKONOMI DAN BISNIS 18, no. 1 (2025): 446–55. https://doi.org/10.51903/e-bisnis.v18i1.2876.

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In the era of data-driven digital commerce, Artificial Intelligence (AI)-based product personalization has become a key strategy to enhance user experience and foster customer loyalty. However, in the Indonesian e-commerce landscape, there remains a lack of empirical understanding of how personalization systems influence long-term user engagement. This study investigates the impact of AI-driven product personalization on customer loyalty among Indonesian e-commerce users. Employing a mixed-methods approach, quantitative data were collected through an online survey of 150 active users, and qual
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Upasana, Das. "AI Powered Consumer Behavior in e-Commerce." International Journal of Contemporary Research in Multidisciplinary 3, no. 4 (2024): 01–13. https://doi.org/10.5281/zenodo.12679257.

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As the e-commerce industry continues to evolve, artificial intelligence (AI) has emerged as a powerful tool for understanding and influencing consumer behavior. This review explores the convergence of e-commerce and consumer behavior, emphasizing the significant influence of AI-driven personalization on market trends. AI-powered technologies have the ability to analyze vast amounts of data in real-time, providing valuable insights into consumer preferences, shopping patterns, and decision-making processes. By leveraging AI algorithms, e-commerce retailers can personalize the shopping experienc
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Dissertations / Theses on the topic "E-commerce Personalization"

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Belcher, Kimberlee Neil. "Interactivity and personalization in product presentation on e-commerce websites." Diss., Columbia, Mo. : University of Missouri-Columbia, 2005. http://hdl.handle.net/10355/4262.

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Thesis (M.A.)--University of Missouri-Columbia, 2005.<br>The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file viewed on (November 27, 2006) Includes bibliographical references.
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Ho, Woo Nancy. "Personality-based design and implementation for personalization of e-Commerce applications." Thesis, University of Ottawa (Canada), 2008. http://hdl.handle.net/10393/27693.

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In commerce, user behaviour modelling is an important component of marketing and advertising. Personalization, a widely used feature of e-Commerce systems, is one aspect of such modelling. Current personalization systems require users to register in order to provide their services; and many of the personalization aspects offered are determined by requiring the user to fill extensive forms regarding their preferences. Although there are a few systems that provide automatic personalization, most focus on the link and content personalization only. Work done in user profiling and determining user
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Geuens, Stijn. "Personalization in e-commerce : a procedure to create and evaluate business relevant recommendation systems." Thesis, Lille 1, 2017. http://www.theses.fr/2017LIL12016/document.

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Les systèmes de recommandation sont très étudiés dans la littérature sur l’apprentissage automatique, ce qui a permis la création de nombreux algorithmes. Cette thèse doctorale va au-delà de simples propositions de nouveaux algorithmes en tirant parti des toutes dernières techniques et en étudiant les interactions de ces techniques avec diverses sources de données. Nous nous sommes penchés sur la création de canevas capables d’aider les universitaires et les décideurs du marché dans le cadre du développement des systèmes de recommandation dans le contexte du e-commerce. Concrètement, cette thè
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Flory, Long Mrs. "A WEB PERSONALIZATION ARTIFACT FOR UTILITY-SENSITIVE REVIEW ANALYSIS." VCU Scholars Compass, 2015. http://scholarscompass.vcu.edu/etd/3739.

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Online customer reviews are web content voluntarily posted by the users of a product (e.g. camera) or service (e.g. hotel) to express their opinions about the product or service. Online reviews are important resources for businesses and consumers. This dissertation focuses on the important consumer concern of review utility, i.e., the helpfulness or usefulness of online reviews to inform consumer purchase decisions. Review utility concerns consumers since not all online reviews are useful or helpful. And, the quantity of the online reviews of a product/service tends to be very large. Manual as
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Magonette, Pierre. "Competitive Advantage Through the Customer Involvement in E-commerce Strategies : A Multiple-Case Study in the European Airline Industry." Thesis, Högskolan i Halmstad, Sektionen för ekonomi och teknik (SET), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-26335.

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Much research show that strategies of personalization aiming to meet customers' needs and behaviors are an important aspect to influence the competitiveness within an industry. However there is a gap of studies on how airlines use customer involvement for creating personalized strategies in the field of e-commerce while enhancing the competitive advantage. This study is aims to describe how airlines can create personalized e-commerce strategies by considering the customer involvement in order to enhance their competitiveness. In conjunction with this first assumption the study aims to demonstr
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Persson, Christian. "Strategies for enhancing consumer interaction in electronic retailing." Doctoral thesis, KTH, Numerical Analysis and Computer Science, NADA, 2001. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3267.

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Brost, Ludvig, Daniel Ludwiszewski, and Ted Oskarsson. "Personlig anpassning vid e-postmarknadsföring : Framställningens och kundrelationens betydelse." Thesis, Jönköping University, JIBS, Business Informatics, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-123.

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<p>Bakgrund: Företag som huvudsakligen agerar med Internet som kontaktkanal möter sällan eller aldrig sina kunder fysiskt, vilket leder till ett behov av att komplettera frånvaron av fysisk kontakt med andra former av kommunikation. Som lösning på detta problem har företag börjat tillämpa personligt anpassad e-postmarknadsföring vid kontakt med konsumenter för att bättre tillgodose kunders behov av relevant information. Samtidigt som personlig anpassning ger möjligheten att erbjuda kunden mervärde och öka kundens lojalitet kan också kunden uppfatta tekniken som falsk och konstruerad. Otillräck
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Müller, Petr. "Metodický postup zavedení doporučovacího systému s využitím nástroje Soyka." Master's thesis, Vysoká škola ekonomická v Praze, 2017. http://www.nusl.cz/ntk/nusl-358790.

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This diploma thesis is focused on content personalization and specifically on recommender systems. The aim of the thesis is to propose a methodology of recommender system implementation in e-commerce using the IT tool Soyka. Functions of personalization tools which directly support recommender systems are identified on the basis of a theoretical description of recommender systems and their technological approaches. Based on these identified functions the tool Soyka is classified. The main contribution of the thesis is the created and published methodology which is ready to be used on real impl
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Elsbernd, Erin. "Just Do It: Female Consumers, E-Commerce, PersonalizationAn Analysis of Nike's Marketing Strategy in China." The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1398743591.

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Kuka, Josefine Frederike. "Personalization of product rankings in e-commerce." Master's thesis, 2019. http://hdl.handle.net/10362/71768.

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Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics<br>Consumers face a large number of choices while shopping online. Studies have shown, that they are already expecting to be targeted with content addressing their personal needs. In a web shop, products are presented as lists based on a selected category or as results of a product search. To support the users in their decision making, they can be provided with a personalized product ranking fitted to their current interests. In this piece of work, three levels of per
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Books on the topic "E-commerce Personalization"

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Carberry, Sandra. User Modeling, Adaptation, and Personalization: 21th International Conference, UMAP 2013, Rome, Italy, June 10-14, 2013 Proceedings. Springer Berlin Heidelberg, 2013.

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Massimo, Zancanaro, Pianesi Fabio, McCalla Gord, and SpringerLink (Online service), eds. User Modeling, Adaptation, and Personalization: 17th International Conference, UMAP 2009, formerly UM and AH, Trento, Italy, June 22-26, 2009. Proceedings. Springer Berlin Heidelberg, 2009.

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Nisar, Tahir M. Personalization and Digital Social Markets. Bloomsbury Publishing Plc, 2025. https://doi.org/10.5040/9781350444447.

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Tahir Nisar presents a cogent, compelling account of recent developments and disruptions within the digital economy, and particularly within the industrial and service sectors.Through an original, overarching framework rooted in the concept of personalization and its antecedents, Nisar identifies radically new forms of relationships, both economic and social, among firms and customers. These new relationships are driving major changes in commercial and industrial firms’ policies and practices, and in turn, in the entire market economy. E-commerce trading, user-generated content, virtual commun
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Kobsa, Alfred, Sarabjot Singh Anand, Dietmar Jannach, and Bamshad Mobasher. Intelligent Techniques for Web Personalization and Recommender Systems in E-Commerce: Papers from the 2007 AAAI Workshop. AAAI Press, 2007.

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User Modeling, Adaptation and Personalization: 22nd International Conference, UMAP 2014, Aalborg, Denmark, July 7-11, 2014. Proceedings. Springer, 2014.

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Book chapters on the topic "E-commerce Personalization"

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Maniadis, Ioannis, Konstantinos N. Vavliakis, and Andreas L. Symeonidis. "Banner Personalization for e-Commerce." In IFIP Advances in Information and Communication Technology. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-19823-7_53.

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Uchyigit, Gulden. "Semantically Enhanced Web Personalization." In Web Mining Applications in E-commerce and E-services. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-88081-3_2.

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Musto, Cataldo, Fedelucio Narducci, Pasquale Lops, Marco de Gemmis, and Giovanni Semeraro. "Content-Based Personalization Services Integrating Folksonomies." In E-Commerce and Web Technologies. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03964-5_21.

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Sae-Tang, Suwimol, and Vatcharaporn Esichaikul. "Web Personalization Techniques for E-commerce." In Active Media Technology. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45336-9_8.

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Cappi, Juan, Gustavo Rossi, Andrés Fortier, and Daniel Schwabe. "Seamless Personalization of E-commerce Applications." In Conceptual Modeling for New Information Systems Technologies. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-46140-x_35.

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Wasilewski, Adam. "Introduction to the Personalization in E-commerce." In Progress in IS. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-67758-8_1.

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Jessenitschnig, Markus, and Markus Zanker. "ISeller: A Flexible Personalization Infrastructure for e-Commerce Applications." In E-Commerce and Web Technologies. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03964-5_31.

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Nasraoui, Olfa, Raghu Krishnapuram, Anupam Joshi, and Tapan Kamdar. "Automatic Web User Profiling and Personalization Using Robust Fuzzy Relational Clustering." In E-Commerce and Intelligent Methods. Physica-Verlag HD, 2002. http://dx.doi.org/10.1007/978-3-7908-1779-9_14.

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Hoelscher, Christoph, and Horst Dietrich. "E-Commerce Personalization and Real-Time Site Monitoring." In Designing Personalized User Experiences in eCommerce. Springer Netherlands, 2004. http://dx.doi.org/10.1007/1-4020-2148-8_7.

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Yu, Chien-Chih. "Service-Oriented Data and Process Models for Personalization and Collaboration in e-Business." In E-Commerce and Web Technologies. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11823865_8.

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Conference papers on the topic "E-commerce Personalization"

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Hemalatha, Raavi, Godi Amulya, and CH S. N. Sai Lalitha. "AI in E-commerce Personalization." In 2024 Second International Conference Computational and Characterization Techniques in Engineering & Sciences (IC3TES). IEEE, 2024. https://doi.org/10.1109/ic3tes62412.2024.10877578.

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Chaudhari, Aarti, and Shital Hajare. "AI-Powered Personalization in E-Commerce." In 2024 2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry (IDICAIEI). IEEE, 2024. https://doi.org/10.1109/idicaiei61867.2024.10842768.

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Shrivastava, Vivek, Prasenjeet Samanta, Pratibha Dimri, Mayank Pant, Sonal Saxena, and Vasim Ahmad. "Data Science for E-Commerce Buyer Segmentation and Personalization of Ads." In 2024 2nd World Conference on Communication & Computing (WCONF). IEEE, 2024. http://dx.doi.org/10.1109/wconf61366.2024.10692027.

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G, Aishwarya Gowda A., Hui-Kai Su, and Wen-Kai Kuo. "Personalized E-commerce: Enhancing Customer Experience through Machine Learning-driven Personalization." In 2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems (ICITEICS). IEEE, 2024. http://dx.doi.org/10.1109/iciteics61368.2024.10624901.

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Zhang, Jiaxin, Pavan Kumar Murugan, Lin Chen, et al. "THE DEVELOPMENT OF SMART WARDROBE: AI-DRIVEN PERSONALIZATION IN E-COMMERCE." In 17th International Conference on Education and New Learning Technologies. IATED, 2025. https://doi.org/10.21125/edulearn.2025.1842.

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T, Logapriya, Frederick Ruby Helen, and B. Lavaraju. "Experimental Evaluation of Artificial Intelligence Assisted E-Commerce Personalization and Recommendation Techniques." In 2024 4th International Conference on Soft Computing for Security Applications (ICSCSA). IEEE, 2024. https://doi.org/10.1109/icscsa64454.2024.00041.

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Nishamali, M. K. C. P., Gihan Saminda Pathirage, Samitha Herath, Penusha Herath, and Jagath Wickramarathne. "A Systematic Study of Machine Learning Applications in E-Commerce UX Personalization." In 2024 4th International Conference on Mobile Networks and Wireless Communications (ICMNWC). IEEE, 2024. https://doi.org/10.1109/icmnwc63764.2024.10872138.

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Sharma, Rishabh, and Abhinav Mishra. "Matrix Factorization vs. Deep Learning Autoencoders: A Performance Analysis in E-commerce Personalization." In 2024 7th International Conference on Circuit Power and Computing Technologies (ICCPCT). IEEE, 2024. http://dx.doi.org/10.1109/iccpct61902.2024.10673071.

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Kumar, Gaurav, G. Devayani, C. Saraswathi, R. Melba Kani, Sandeep Dongre, and M. S. Mohamed Mallick. "Exploring Consumer Behavior in E-Commerce with AI Personalization and Market Trends Analysis Models." In 2025 3rd International Conference on Integrated Circuits and Communication Systems (ICICACS). IEEE, 2025. https://doi.org/10.1109/icicacs65178.2025.10967904.

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Matharu, Harmeet, Zameer Pasha, Mohd Aarif, L. Natrayan, S. Kaliappan, and I. Infant Raj. "Developing an AI-Driven Personalization Engine for Real-Time Content Marketing in E-commerce Platforms." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10725400.

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