Search Results - subject:"‘Personalization’"

Recommendation system using NLP based on emotion detection by CNN

Jacophine Susmi, S.; Ramya, S. S.; Wilfred Blessing, N. R. - In: AI Integration for Business Sustainability : For a …, (pp. 249-259). 2025

Persistent link: https://www.econbiz.de/10015412412

Crowding Out the Truth? A Simple Model of Misinformation, Polarization, and Meaningful Social Interactions

Germano, Fabrizio; Gómez, Vicenç; Sobbrio, Francesco - 2022

theoretical framework to evaluate the effect of popularity and personalization parameters on measures of platform and user welfare …

Persistent link: https://www.econbiz.de/10013470270

Factors influencing continuance intention of online shopping of generation Y and Z during the new normal in Vietnam

Thi, Binh Nguyen; Tran, Thi Lan Anh; Tran, Thi Thu Hien; … - In: Cogent Business & Management 9 (2022) 1, pp. 1-25

served as a barrier. Notably, the barrier was found to affect Gen Y's repurchase intention more severely. Personalization was …

Persistent link: https://www.econbiz.de/10014505410

Toward customer hyper-personalization experience — A data-driven approach

Mendia, J. M. Valdez; Flores-Cuautle, J. J. A. - In: Cogent Business & Management 9 (2022) 1, pp. 1-16

Today's omnichannel business models incorporate physical and digital touchpoints interacting with customers. A hyper-personalization …, when a hyper-personalization organizational plan is put in place, it serves two main functions: to deliver personalized … hyper-personalization strategy: data foundation, decisions, design, and distribution. While customer master data management …

Persistent link: https://www.econbiz.de/10014505429

Personalized fundraising: A field experiment on threshold matching of donations

Adena, Maja; Huck, Steffen - 2022

function of their personal characteristics and demonstrate the benefits of personalization as opposed to setting a general …

Persistent link: https://www.econbiz.de/10013197568

Network-Enabled Sequential Data Acquisition for High-Dimensional Recommender Systems

Cao, Junyu; Leng, Yan - 2022

-commerce platforms, with notable examples including recommender systems, consumer profiling, and assortment personalization. We formulate …

Persistent link: https://www.econbiz.de/10014084891

A Comprehensive Insight on Machine Learning enabled Internet of Things Recommender Systems (IoTRS)

Ahlawat, Partibha; Rana, Chhavi - 2022

machine learning techniques. Personalization and recommendations services in different application domains are enhanced by the …

Persistent link: https://www.econbiz.de/10014085971

Privacy-Preserving Personalized Recommender Systems

Fu, Xingyu; Chen, Ningyuan; Gao, Pin; Li, Yang - 2022

Although personalized recommender systems are vital for many online platforms, they lead to controversial societal issues such as privacy breaches. In response to such concerns, there is a clear regulatory trend in calling for stringent privacy protection mechanisms implanted in such algorithms....

Persistent link: https://www.econbiz.de/10014030079

The Dark Side of Algorithms? The Effect of Recommender Systems on Online Investor Behaviors

Zhu, Ruiqi Rich; He, Cheng; Hu, Yu Jeffrey - 2022

Despite the widespread adoption of recommender systems by online investment platforms, empirical research into their impact on online investors’ behaviors is scarce. Using data from a global e-commerce platform, the authors of this study adopt a regression discontinuity design and causally...

Persistent link: https://www.econbiz.de/10014235805

Targeted Advertising as an Implicit Recommendation and Personal Data Opt-Out

Ning, Z. Eddie; Shin, Jiwoong; Yu, Jungju - 2022

Advances in data collection and algorithms help advertisers to better target individual consumers by predicting each consumer's preferences. We first show that when consumers have uncertainties about their preferences, an ad targeted to a consumer carries an implicit message: the algorithm...

Persistent link: https://www.econbiz.de/10014236645