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Advisor(s)
Abstract(s)
Information and Communication Technologies (ICT) have revolutionised the tourism domain, providing a wide
set of new services for tourists and tourism businesses. Both tourists and tourism businesses use dedicated
tourism platforms to search and share information generating, constantly, new tourism crowdsourced data. This
crowdsourced information has a huge influence in tourist decisions. In this context, the paper proposes a stream
recommendation engine supported by crowdsourced information, adopting Stochastic Gradient Descent (SGD)
matrix factorisation algorithm for rating prediction. Additionally, we explore different: (i) profiling approaches
(hotel-based and theme-based) using hotel multi-criteria ratings, location, value for money (VfM) and sentiment
value (StV); and (ii) post-recommendation filters based on hotel location, VfM and StV. The main contribution
focusses on the application of post-recommendation filters to the prediction of hotel guest ratings with both hotel
and theme multi-criteria rating profiles, using crowdsourced data streams. The results show considerable accuracy and classification improvement with both hotel-based and theme-based multi-criteria profiling together
with location and StV post-recommendation filtering. While the most promising results occur with the hotelbased version, the best theme-based version shows a remarkable memory conciseness when compared with its
hotel-based counterpart. This makes this theme-based approach particularly appropriate for data streams.
The abstract completely needs to be rewritten. It does not provide a clear view of the problem and its solutions the researchers proposed. In addition, it should cover five main elements, introduction, problem statement, methodology, contributions and results. Done.
Description
Keywords
Profiling Recommendation Data streams Post-filtering
Citation
Publisher
Elsevier