A 2020 perspective on 'Online guest profiling and hotel recommendation': Reliability, scalability, traceability and transparency
| Autor: | Fátima Leal, Bruno Veloso, Juan C. Burguillo, Benedita Malheiro |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Traceability
Computer Networks and Communications Computer science 02 engineering and technology Crowdsourcing 020204 information systems Management of Technology and Innovation 0502 economics and business 0202 electrical engineering electronic engineering information engineering Profiling (information science) Post-filtering Marketing business.industry Data stream mining Profiling 05 social sciences Recommendation Data science Computer Science Applications Stochastic gradient descent Scalability 050211 marketing business Predictive modelling Tourism |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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