Extracting and ranking travel tips from user-generated reviews

Ido Guy, Alexander Nus, Avihai Mejer, Fiana Raiber

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

21 Scopus citations

Abstract

User-generated reviews are a key driving force behind some of the leading websites, such as Amazon, TripAdvisor, and Yelp. Yet, the proliferation of user reviews in such sites also poses an information overload challenge: many items, especially popular ones, have a large number of reviews, which cannot all be read by the user. In this work, we propose to extract short practical tips from user reviews. We focus on tips for travel attractions extracted from user reviews on TripAdvisor. Our method infers a list of templates from a small gold set of tips and applies them to user reviews to extract tip candidates. For each attraction, the associated candidates are then ranked according to their predicted usefulness. Evaluation based on labeling by professional annotators shows that our method produces high-quality tips, with good coverage of cities and attractions.

Original languageEnglish
Title of host publication26th International World Wide Web Conference, WWW 2017
PublisherInternational World Wide Web Conferences Steering Committee
Pages987-996
Number of pages10
ISBN (Print)9781450349130
DOIs
StatePublished - 1 Jan 2017
Event26th International World Wide Web Conference, WWW 2017 - Perth, Australia
Duration: 3 Apr 20177 Apr 2017

Publication series

Name26th International World Wide Web Conference, WWW 2017

Conference

Conference26th International World Wide Web Conference, WWW 2017
Country/TerritoryAustralia
CityPerth
Period3/04/177/04/17

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