Pharmabroad: A Companion Chatbot for Identifying Pharmaceutical Products When Traveling Abroad

Ruf, Boris; Sammarco, Matteo; Aigrain, Jonathan; Detyniecki, Marcin

doi:10.1007/978-3-030-36737-4_18

Cited by 8 publications

(8 citation statements)

References 7 publications

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“…Ner_spacy gives the F1score of the query parameter extraction of 86.01% in Adobot and 76.1% in Smabot. These results show that Adobot and Smabot are quite competitive with some recently published chatbots such as Nigam et al (2019) with F1-score of 77.63% for intent classification and 82.24% for query parameter extraction; Nguyen and Shcherbakov (2021) with F1-score of 86.3% for intent classification and 81.5% for query parameter extraction; Morgan et al (2018) with F1-score of 98% for intent classification; and Ruf et al (2020) with F1-score of 87% for query parameter extraction.…”

Section: Technical Experimentsmentioning

confidence: 70%

A knowledge-based model for context-aware smart service systems

Dinh

Thi

Pham-Nguyen

et al. 2021

Journal of Information and Telecommunication

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The advancement of the Internet of Things, big data, and mobile computing leads to the need for smart services that enable the context awareness and the adaptability to their changing contexts. Today, designing a smart service system is a complex task due to the lack of an adequate model support in awareness and pervasive environment. In this paper, we present the concept of a context-aware smart service system and propose a knowledge model for context-aware smart service systems. The proposed model organizes the domain and context-aware knowledge into knowledge components based on the three levels of services: Services, Service system, and Network of service systems. The knowledge model for context-aware smart service systems integrates all the information and knowledge related to smart services, knowledge components, and context awareness that can play a key role for any framework, infrastructure, or applications deploying smart services. In order to demonstrate the approach, two case studies about chatbot as context-aware smart services for customer support are presented.

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Section: Technical Experimentsmentioning

confidence: 70%

A knowledge-based model for context-aware smart service systems

Dinh

Thi

Pham-Nguyen

et al. 2021

Journal of Information and Telecommunication

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“…Clarizia et al (2019) introduced a CB relying on a context-aware system able to recommend contents and services to increase the promotion of cultural heritage. Finally Ruf et al (2020) realized a companion CB to help travelers decoding medical drugs sold in the host country, linking them with the corresponding trade name sold in the traveler's home country.…”

Section: Slr Resultsmentioning

confidence: 99%

Leveraging inter-tourists interactions via chatbots to bridge academia, tourism industries and future societies

Calvaresi

Kineber

Calbimonte

et al. 2021

JTF

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PurposeThe tourism and hospitality sectors are experiencing radical innovation boosted by the advancements in Information and Communication Technologies. Increasingly sophisticated chatbots are introducing novel approaches, re-shaping the dynamics among tourists and service providers, and fostering a remarkable behavioral change in the overall sector. Therefore, the objective of this paper is two-folded: (1) to highlight the academic and industrial standing points with respect to the current chatbots designed/deployed in the tourism sector and (2) to develop a proof-of-concept embodying the most prominent opportunities in the tourism sector.Design/methodology/approachThis work elaborates on the outcomes of a Systematic Literature Review (SLR) and a Focus Group (FG) composed of experts from the tourism industry. Moreover, it presents a proof-of-concept relying on the outcomes obtained from both SLR and FG. Eventually, the proof-of-concept has been tested with experts and practitioners of the tourism sector.FindingsAmong the findings elicited by this paper, we can mention the quick evolution of chatbot-based solutions, the need for continuous investments, upskilling, system innovation to tackle the eTourism challenges and the shift toward new dimensions (i.e. tourist-to-tourist-to-chatbot and personalized multi-stakeholder systems). In particular, we focus on the need for chatbot-based activity and thematic aggregation for next-generation tourists and service providers.Originality/valueBoth academic- and industrial-centered findings have been structured and discussed to foster the practitioners' future research. Moreover, the proof-of-concept presented in the paper is the first of its kind, which raised considerable interest from both technical and business-planning perspectives.

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“…CBTs also require a remarkable technical knowledge. For example, modelling user and system dynamics [3], identifying and designing the right architecture [2], modelling and automating processes and testing [3,29], and modelling and implementing data collection, compliance, and organization [2,17,35].…”

Section: Srq5: Desideratamentioning

confidence: 99%

“…Given that CBTs are evolving at a fast pace, to deliver incremental functionalities or adding new ones to enhance the user experience has often been set as a priority goal. Examples include creating a novel tone-aware chatbot that generates toned responses to user requests [18], automatizing testing the functionalities of CBTs [3], introducing a chatbot based on a context-aware system able to recommend contents and services to increase the promotion of cultural heritage [11], or the realization of a companion chatbot to help travelers decoding medical drug boxes sold in the host country, linking them with the corresponding trade name sold in the traveler's home country [35].…”

Section: Srq6: Goalsmentioning

confidence: 99%

“…On the one hand, the socio-technical functionalities (STF) address what can be related to the service management by performing basic tasks such as booking a room, answering FAQs [4,12,33], understand and answer customer queries instantly [33], ordering meals or drinks [31], controlling the room temperature, lighting, taxi booking, and itinerary planning [10,31], and identifying a corresponding medical product from the user's home market [35]. On the other hand, they can solely communicate to the client messages pre-arrival, throughout their stay and post-checkout [4], generating toned responses to user requests based on their humor using the seq2seq model implemented with recurrent neural networks (RNN), such as the Long Short-Term Memory (LSTM) or the Gated Recurrent Units (GRU) model [18], providing necessary information to offer a better touristic experience [26], even adapting the user interface according to the visitor's backgrounds for better personalization [11].…”

Section: Srq7: Services Realizedmentioning

confidence: 99%

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The Evolution of Chatbots in Tourism: A Systematic Literature Review

Calvaresi

Kineber

Calbimonte

et al. 2021

Information and Communication Technologies in Tourism 2021

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In the last decade, Information and Communication Technologies have revolutionized the tourism and hospitality sector. One of the latest innovations shaping new dynamics and fostering a remarkable behavioral change in the interaction between the service provider and the tourist is the employment of increasingly sophisticated chatbots. This work analyzes the most recent systems presented in the literature (since 2016) investigated via 12 research questions. The often appreciated quick evolution of such solutions is the primary outcome. However, such technological and financial fast-pace requires continuous investments, upskilling, and system innovation to tackle the eTourism challenges, which are shifting towards new dimensions.

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Pharmabroad: A Companion Chatbot for Identifying Pharmaceutical Products When Traveling Abroad

Cited by 8 publications

References 7 publications

A knowledge-based model for context-aware smart service systems

A knowledge-based model for context-aware smart service systems

Leveraging inter-tourists interactions via chatbots to bridge academia, tourism industries and future societies

The Evolution of Chatbots in Tourism: A Systematic Literature Review

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