A comprehensive mechanism for hotel recommendation to achieve personalized search engine

Abstract

Search engines are as important as recommender systems for hotel selections. However, the recommended lists of search engines are usually non-personalized and low accuracy. In order to deal with these issues in search engines, a comprehensive mechanism for hotel recommendation is proposed. In this mechanism, we consider users' personalized preferences by identifying users' attributes about interest, trust and consumption capacity. Meanwhile, the quantification method for each attribute is presented by using fuzzy theory. Moreover, this paper improves the method to evaluate the hotel, which respects to the criteria price, rating, and online review by using fuzzy theory. In addition, this proposed approach uses TOPSIS, a classical multi-criteria decision making method, to improve the accuracy further. Finally, a case study is conducted based on Tripadvisor.com to illustrate the validity of the proposed method for hotel recommendation in search engines. The results of the case study indicate that it not only solves the problem of non-personalization, but also improves the accuracy in search engine.

AsCDPR: a novel framework for ratings and personalized preference hotel recommendation using cross-domain and aspect-based features

“…Integrating rating scores and reviews is believed to be able to provide more precise and superior recommendation results following user preferences. Huang et al (2018) considered customers' personalized preferences about interest, trust and consumption capacity and hotels' attributes in the criteria price, ratings and reviews and then employed technique for order of preference by similarity to ideal solution to recommend hotels to customers. Liu et al (2019) exploited the shared latent structures between customers' multiaspect ratings and review texts.…”

Trust Model for Online Reviews of Tourism Services and Evaluation of Destinations

“…Many studies (e.g., Ham et al 2019) confirm the significant influence of online reviews of service quality, attractions and destinations on the decision-making process of potential tourism participants. Applications are created that extract useful information from reviews, such as the evaluation of attractions on TripAdvisor (Guy et al 2017) and multi-criteria evaluation of the quality and cost-effectiveness of hotel services (Huang et al 2018). On the contrary, some studies (Ert et al 2016) question the influence of reviews in relation to other factors and demonstrate the decisive influence of photographs on customer decisions, as in the case of Airbnb.…”