A 2020 perspective on "Online guest profiling and hotel recommendation": Reliability, Scalability, Traceability and Transparency

• Type: Article in International Scientific Journal
• Date: 2020

Authors

Veloso, BM (Author) FEP; Leal, F (Author) Other; Malheiro, B (Author) Other; Carlos Burguillo, JC (Author) Other

Journal

Title: Electronic Commerce Research and Applications

Vol. 40

ISSN: 1567-4223

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 6 Citations

Scopus - 9 Citations

Other information

Authenticus ID: P-00R-P87

DOI: 10.1016/j.elerap.2020.100957

Abstract (EN): Tourism crowdsourcing platforms accumulate and use large volumes of feedback data on tourism-related services to provide personalized recommendations with high impact on future tourist behavior. Typically, these recommendation engines build individual tourist profiles and suggest hotels, restaurants, attractions or routes based on the shared ratings, reviews, photos, videos or likes. Due to the dynamic nature of this scenario, where the crowd produces a continuous stream of events, we have been exploring stream-based recommendation methods, using stochastic gradient descent (SGD), to incrementally update the prediction models and post-filters to reduce the search space and improve the recommendation accuracy. In this context, we offer an update and comment on our previous article (Veloso et al., 2019a) by providing a recent literature review and identifying the challenges laying ahead concerning the online recommendation of tourism resources supported by crowdsourced data.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 2