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dc.contributor.authorCamilleri, Mark Anthony-
dc.contributor.authorTroise, Ciro-
dc.date.accessioned2023-01-10T14:57:18Z-
dc.date.available2023-01-10T14:57:18Z-
dc.date.issued2023-
dc.identifier.citationCamilleri, M.A. &Troise, C. (2023). Chatbot recommender systems in tourism: A systematic review and a benefit-cost analysis. 8th International Conference on Machine Learning Technologies (ICMLT 2023), New York.en_GB
dc.identifier.isbn9781450398336-
dc.identifier.urihttps://www.um.edu.mt/library/oar/handle/123456789/105032-
dc.description.abstractThis research is focused on the utilization of artificially intelligent (AI), customer service chatbots in travel, tourism and hospitality. Rigorous criteria were used to search, screen, extract and synthesize articles on conversational, automated systems. The results shed light on the most-cited articles on the use of “chatbots” and “tourism” or “hospitality”. The researchers scrutinize the extracted articles, synthesize the findings and outline the pros and cons of using these interactive technologies. This contribution implies that there is scope for tourism businesses to continue improving their online customer services in terms of their efficiency and responsiveness to consumers and prospects. For the time being, AI chatbots are still not in a position to replace human agents in all service interactions as they cannot resolve complex queries and complaints. However, works are in progress to improve their verbal, vocal and anthropomorphic capabilities to deliver a better consumer experience.en_GB
dc.language.isoenen_GB
dc.publisherACM Digital Libraryen_GB
dc.rightsinfo:eu-repo/semantics/openAccessen_GB
dc.subjectArtificial intelligenceen_GB
dc.subjectCustomer servicesen_GB
dc.subjectOnline information servicesen_GB
dc.subjectWeb sitesen_GB
dc.subjectMachine learningen_GB
dc.subjectConversation analysisen_GB
dc.subjectHuman-computer interactionen_GB
dc.titleChatbot recommender systems in tourism : a systematic review and a benefit-cost analysisen_GB
dc.typeconferenceObjecten_GB
dc.rights.holderThe copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder.en_GB
dc.bibliographicCitation.conferencename8th International Conference on Machine Learning Technologies (ICMLT 2023)en_GB
dc.bibliographicCitation.conferenceplaceStockholm, Sweden. 10-12/03/2023.en_GB
dc.description.reviewedpeer-revieweden_GB
Appears in Collections:Scholarly Works - FacMKSCC

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