On the value of purpose-orientation and focus on locals in recommending leisure activities

Beatrice Valeri, Fabio Casati, Florian Daniel

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Recommender systems are omnipresent today, especially on the Web, and the quality of their recommendations is crucial for user satisfaction. Unlike most works on the topic, in this article we do not focus on the algorithmic side of the problem (i.e., searching for the algorithm that better learns from the collected user feedback) and instead study the importance of the data in input to the algorithms, identifying the information that should be collected from users to build better recommendations. We study restaurant recommendations for locals and show that fine-tuned data and state-of-the-art algorithms can outperform the leading recommendation service, TripAdvisor. The findings make a case for better-thought and purpose-tailored data collection techniques.

Original languageEnglish
Pages (from-to)63-74
Number of pages12
JournalJournal of Web Engineering
Volume16
Issue number1-2
Publication statusPublished - 1 Mar 2017
Externally publishedYes

    Fingerprint

Keywords

  • Data collection
  • Mobile recommendations
  • Recommender systems
  • Restaurants
  • TripAdvisor

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Computer Networks and Communications

Cite this