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Incremental Hotel Recommendation with Inter-guest Trust and Similarity Post-filtering
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Resumo(s)
Crowdsourcing has become an essential source of information for tourists and tourism industry. Every day, large volumes of data
are exchanged among stakeholders in the form of searches, posts, shares,
reviews or ratings. Specifically, this paper explores inter-guest trust and
similarity post-filtering, using crowdsourced ratings collected from the
Expedia and TripAdvisor platforms, to improve hotel recommendations
generated by incremental collaborative filtering. First, the profiles of hotels and guests are created using multi-criteria ratings and inter-guest
trust and similarity. Next, incremental model-based collaborative filtering is adopted to predict unknown hotel ratings based on the multicriteria ratings and, finally, post-recommendation filtering sorts the generated predictions based on the inter-guest trust and similarity. The proposed method was tested both off-line (post-processing) and on-line (real
time processing) for performance comparison. The results highlight: (i)
the increase of the quality of recommendations with the inter-guest trust
and similarity; and (ii) the decrease of the predictive errors with the online incremental collaborative filtering. Thus, this work contributes with
a novel method, integrating incremental collaborative filtering and interguest trust and similarity post-filtering, for on-line hotel recommendation
based on multi-criteria crowdsourced rating streams.
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Editora
Springer