In a ubiquitous computing scenario, characterized by pervasive technologies, tourists can get assistance from mobile technologies in planning their trips. In a context where more and more people own smartphones, tourists expect to get personalized suggestions just in time whenever and wherever they need. To be effective, mobile applications for travel recommendation should consider both the variability of the user’s interests and an effective way to express them while interacting with the environment. This paper presents LOOKER, a mobile recommender system for tourism and travel-related services that considers the above-described issues. It is an adaptable application developed for the Android platform, which takes into account basic contextual information such as location and time, and implements a content-based filtering (CBF) strategy to make personalized suggestions based on the user’s tourism-related user-generated content (UGC) s/he diffuses on social media. Specifically, the CBF strategy implemented in LOOKER is based on a multi-layer user profile, where the layers representing distinct travel-related service categories (e.g., restaurants, hotels, points of interest) are modeled via language models that are defined on the basis of the captured UGC. This allows inferring the interests and the opinions of travelers about the available items. To evaluate the usefulness and the usability of the LOOKER mobile application, user studies have been conducted. The positive outcomes that have been obtained illustrate the potentials of LOOKER.

Missaoui, S., Kassem, F., Viviani, M., Agostini, A., Faiz, R., Pasi, G. (2019). LOOKER: a mobile, personalized recommender system in the tourism domain based on social media user-generated content. PERSONAL AND UBIQUITOUS COMPUTING, 23(2), 181-197 [10.1007/s00779-018-01194-w].

LOOKER: a mobile, personalized recommender system in the tourism domain based on social media user-generated content

MISSAOUI, SONDESS;Viviani, Marco
;
Agostini, Alessandra;Pasi, Gabriella
2019

Abstract

In a ubiquitous computing scenario, characterized by pervasive technologies, tourists can get assistance from mobile technologies in planning their trips. In a context where more and more people own smartphones, tourists expect to get personalized suggestions just in time whenever and wherever they need. To be effective, mobile applications for travel recommendation should consider both the variability of the user’s interests and an effective way to express them while interacting with the environment. This paper presents LOOKER, a mobile recommender system for tourism and travel-related services that considers the above-described issues. It is an adaptable application developed for the Android platform, which takes into account basic contextual information such as location and time, and implements a content-based filtering (CBF) strategy to make personalized suggestions based on the user’s tourism-related user-generated content (UGC) s/he diffuses on social media. Specifically, the CBF strategy implemented in LOOKER is based on a multi-layer user profile, where the layers representing distinct travel-related service categories (e.g., restaurants, hotels, points of interest) are modeled via language models that are defined on the basis of the captured UGC. This allows inferring the interests and the opinions of travelers about the available items. To evaluate the usefulness and the usability of the LOOKER mobile application, user studies have been conducted. The positive outcomes that have been obtained illustrate the potentials of LOOKER.
Articolo in rivista - Articolo scientifico
Content-based filtering; Language models; Mobile recommender systems; Personalization; Social media; User-generated content;
Content-based filtering; Language models; Mobile recommender systems; Personalization; Social media; User-generated content; Hardware and Architecture; Computer Science Applications1707 Computer Vision and Pattern Recognition; Management Science and Operations Research
English
4-gen-2019
2019
23
2
181
197
reserved
Missaoui, S., Kassem, F., Viviani, M., Agostini, A., Faiz, R., Pasi, G. (2019). LOOKER: a mobile, personalized recommender system in the tourism domain based on social media user-generated content. PERSONAL AND UBIQUITOUS COMPUTING, 23(2), 181-197 [10.1007/s00779-018-01194-w].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/214623
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