Barbu, Catalin-Mihai; Ziegler, Jürgen:
Designing Interactive Visualizations of Personalized Review Data for a Hotel Recommender System
In: Proceedings of the Workshop on Recommenders in Tourism : co-located with the 12th ACM Conference on Recommender Systems (RecSys 2018) / Neidhardt, Julia; Wörndl, Wolfgang; Kuflik, Tsvi; Zanker, Markus (Hrsg.). - RecTour 2018: 3rd Workshop on Recommenders in Tourism, 7. October 2018, Vancouver - Aachen: RWTH, 2018 - (CEUR Workshop Proceedings ; 2222), S. 7 - 12
2018Buchaufsatz/Kapitel in TagungsbandOA Gold
InformatikFakultät für Ingenieurwissenschaften » Informatik und Angewandte Kognitionswissenschaft » Informatik » Interaktive Systeme / Interaktionsdesign
Titel in Englisch:
Designing Interactive Visualizations of Personalized Review Data for a Hotel Recommender System
Autor(in):
Barbu, Catalin-MihaiLSFSCOPUS; Ziegler, JürgenLSFSCOPUS
Open Access?
OA Gold
URN:
Scopus ID:
WWW URL
Sprache des Textes
Englisch
Schlagwort, Thema:
Recommender systems ; Personalized reviews ; Interactive visualization ; Multimode networks ; Trustworthiness ; Tourism

Abstract in Englisch:

Online reviews extracted from social media are being used increasingly in recommender systems, typically to enhance prediction accuracy. A somewhat less studied avenue of research aims to investigate the underlying relationships that arise between users, items, and the topics mentioned in reviews. Identifying these--often implicit--relationships could be beneficial for at least a couple of reasons. First, they would allow recommender systems to personalize reviews based on a combination of both topic and user similarity. Second, they can facilitate the development of novel interactive visualizations that complement and help explain recommendations even further. In this paper, we report on our ongoing work to personalize user reviews and visualize them in an interactive manner, using hotel recommending as an example domain. We also discuss several possible interactive mechanisms and consider their potential benefits towards increasing users' satisfaction.

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