Multi-view Latent Learning Applied to Fashion Industry

Snakes and Ladders: Unpacking the Personalisation-Privacy Paradox in the Context of AI-Enabled Personalisation in the Physical Retail Environment

“…Therefore, customers may feel less overwhelmed by choice in the physical environment. Moreover, some of our interviewees seemed to associate fashion shopping in the physical environment with an hedonic experience (Gardino et al, 2021 ), rather than a functional one. The pleasant nature of in-store shopping may explain the reduced importance of time savings in AI-EP vs. online personalisation.…”

“…In addition, they may purposefully provide false information, such as using a false name or birth date (Miltgen & Smith, 2019), when firms attempt to collect personal data that they deem private. Boratto et al, 2018;Castillo et al, 2020;Crick et al, 2019;Dwivedi et al, 2021;Gardino et al, 2021;Griva et al, 2021 Many challenges exist for deployment of AI to process data efficiently and effectively, such as poor data availability, lack of skills and leadership buy-in, cost of deployment and ethical and regulatory restrictions Seasonal trends make prediction difficult and unstable, and can be dramatically influenced by a broad range of factors, as witnessed during the Covid-19 pandemic The gap between the AI promise and reality could result in customer backlash and reputation tarnishing, which could have significant, and long lasting, negative impact for firms. Yet, not many studies focus on consumer perceptions Digital Personalisation Ameen et al, 2022;Boerman et al, 2017;Riegger et al, 2021;Sutanto et al, 2013;van de Sanden et al, 2019;Wirtz et al, 2018 Personalisation can impress as well as frustrate customers, who are seeking offers unique to them, as derived by AI Studies examine personalisation in controlled experiments, outside of the shopping environment, and have yet to examine the in-store experience Privacy Aguirre et al, 2016;Awad & Krishnan, 2006;Castelo et al, 2019;De Bruyn et al, 2020;…”

“…Artificial intelligence (AI) is expected to transform business practice in in-store retailing (Davenport et al, 2020), by bringing to the physical retail environment the kind of mass personalisation that is already common in online commerce (Kumar et al, 2017). Personalisation benefits retailers because targeted messages get noticed amid the noise of other communications (Balan & Mathew, 2020), increase sales, and support customer intimacy, involvement with the brand (Gardino et al, 2021) and customer loyalty (Pappas et al, 2018). Moreover, campaign response can be monitored directly and corrective action can be taken promptly, thus improving conversion rate (Chou & Shao, 2021).…”

Breakthroughs on Cross-Cutting Data Management, Data Analytics, and Applied Data Science

“…efficient data models, data processing pipelines and architectures to integrate standard and big data sources (Jovanovic et al 2020) as well as to improve resource utilization and aggregate performance in shared environments (Michiardi et al 2020); predictive analytics to forecast product demand in the fashion industry (Gardino et al 2020) and techniques to deal with the lack of annotated data for sensor-based human activity recognition (Prabono et al 2020); text data processing to assess the performance of text storage systems through a generic benchmark (Truicȃ et al 2020) and innovative solutions to deal with specific use cases such as the legal domain (Bordino et al 2020); novel approaches for mining social media to support intelligent transportation systems (Vallejos et al 2020) and digging deep the IoT scenario (Ustek-Spilda et al 2020); -solutions to deal with privacy issues in distance learning systems (Preuveneers et al 2020).…”

“…In this context, paper (Gardino et al 2020) proposes a method for predicting product demand in the fashion industry. The proposed prediction method, called multi-VIew Bridge Estimation (VIBE), takes advantage of the existence of multiple views on items, i.e., sets of homogeneous features.…”