Item Ownership Relationship Semantic Learning Strategy for Personalized Service Robot

Wu, Hao; Chen, Zhao-Wei; Tian, Guohui; Ma, Qing; Menglin, Jiao

doi:10.1007/s11633-019-1206-7

Cited by 4 publications

(2 citation statements)

References 27 publications

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“…However, in most cases, the services provided by service robots remain standardized, which may negatively affect the customer experience (Hu et al , 2021). How to incorporate AIP into customer–robot interactions in order to improve the customer experience in the service setting is a crucial inquiry that requires further research in the future (Hu et al , 2020; Wu et al , 2020). Moreover, recent research identifies that individual differences (e.g.…”

Section: Future Research Agendamentioning

confidence: 99%

Artificial intelligence-enabled personalization in interactive marketing: a customer journey perspective

Gao

Liu

2022

JRIM

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PurposeArtificial intelligence (AI) technology has revolutionized customers' interactive marketing experience. Although there have been a substantial number of studies exploring the application of AI in interactive marketing, personalization as an important concept remains underexplored in AI marketing research and practices. This study aims to introduce the concept of AI-enabled personalization (AIP), understand the applications of AIP throughout the customer journey and draw up a future research agenda for AIP.Design/methodology/approachDrawing upon Lemon and Verhoef's customer journey, the authors explore relevant literature and industry observations on AIP applications in interactive marketing. The authors identify the dilemmas of AIP practices in different stages of customer journeys and make important managerial recommendations in response to such dilemmas.FindingsAIP manifests itself as personalized profiling, navigation, nudges and retention in the five stages of the customer journey. In response to the dilemmas throughout the customer journey, the authors developed a series of managerial recommendations. The paper is concluded by highlighting the future research directions of AIP, from the perspectives of conceptualization, contextualization, application, implication and consumer interactions.Research limitations/implicationsNew conceptual ideas are presented in respect of how to harness AIP in the interactive marketing field. This study highlights the tensions in personalization research in the digital age and sets future research agenda.Practical implicationsThis paper reveals the dilemmas in the practices of personalization marketing and proposes managerial implications to address such dilemmas from both the managerial and technological perspectives.Originality/valueThis is one of the first research papers dedicated to the application of AI in interactive marketing through the lenses of personalization. This paper pushes the boundaries of AI research in the marketing field. Drawing upon AIP research and managerial issues, the authors specify the AI–customer interactions along the touch points in the customer journey in order to inform and inspire future AIP research and practices.

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Section: Future Research Agendamentioning

confidence: 99%

Artificial intelligence-enabled personalization in interactive marketing: a customer journey perspective

Gao

Liu

2022

JRIM

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“…The identify action uses facial recognition and is used to identify expressions [54], micro-expressions [55], emotions [56] and negative expression [57]. Hand recognition is used to identify weapons [58], [59] and people [60]. Finally, recognition focused on the rest of body parts can be identified skeletal posture [61], neuronal illness or social actions [62], as social touch [63].…”

Section: ) Applicationsmentioning

confidence: 99%

A Survey on Intelligent Gesture Recognition Techniques

et al. 2022

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Gesture recognition is an ideal means of interaction because it allows users not to have to make contact with any surface, which is a safe and hygienic means, especially in the pandemic situation that is occurring worldwide. However, gesture recognition is not a new discipline and it has been researched for many years but this type of interaction has not succeeded in replacing the keyboard and mouse. It is very useful to know about the advances that are being made with artificial intelligence in gesture recognition to be able to perform a more robust and reliable gesture recognition with a low response time. As it is, deep learning is being integrated into various areas to increase improvement in performance and one such area is artificial intelligence. In this way, there is the possibility that in the future the recognition of gestures will be a viable option as a means of daily interaction for the user and the main objective of this paper is to contribute to that process. For this reason, this study has analyzed 571 papers related to gesture recognition and artificial intelligence. This analysis has extracted relevant information related to scientific production, such as the most productive authors and journals or the most pertinent articles on the subject. Furthermore, we have developed our own model, which shows the relationship between the types of gesture recognition and the artificial intelligence techniques that have been applied for this task.

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DiscoStyle: Multi-level Logistic Ranking for Personalized Image Style Preference Inference

Zhang

Liu

2020

Int. J. Autom. Comput.

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Item Ownership Relationship Semantic Learning Strategy for Personalized Service Robot

Cited by 4 publications

References 27 publications

Artificial intelligence-enabled personalization in interactive marketing: a customer journey perspective

Artificial intelligence-enabled personalization in interactive marketing: a customer journey perspective

A Survey on Intelligent Gesture Recognition Techniques

DiscoStyle: Multi-level Logistic Ranking for Personalized Image Style Preference Inference

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