SCIENCE@home

HotelRec: a Novel Very Large-Scale Hotel Recommendation Dataset

Diego Antognini,B. Faltings

Published 2020 in International Conference on Language Resources and Evaluation

ABSTRACT

Today, recommender systems are an inevitable part of everyone’s daily digital routine and are present on most internet platforms. State-of-the-art deep learning-based models require a large number of data to achieve their best performance. Many datasets fulfilling this criterion have been proposed for multiple domains, such as Amazon products, restaurants, or beers. However, works and datasets in the hotel domain are limited: the largest hotel review dataset is below the million samples. Additionally, the hotel domain suffers from a higher data sparsity than traditional recommendation datasets and therefore, traditional collaborative-filtering approaches cannot be applied to such data. In this paper, we propose HotelRec, a very large-scale hotel recommendation dataset, based on TripAdvisor, containing 50 million reviews. To the best of our knowledge, HotelRec is the largest publicly available dataset in the hotel domain (50M versus 0.9M) and additionally, the largest recommendation dataset in a single domain and with textual reviews (50M versus 22M). We release HotelRec for further research: https://github.com/Diego999/HotelRec.

PUBLICATION RECORD

  • Publication year

    2020

  • Venue

    International Conference on Language Resources and Evaluation

  • Publication date

    2020-02-17

  • Fields of study

    Computer Science

  • Identifiers
    arXiv 2002.06854
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

CITATION MAP

EXTRACTION MAP

CLAIMS

  • No claims are published for this paper.

CONCEPTS

  • No concepts are published for this paper.

REFERENCES

Showing 1-31 of 31 references · Page 1 of 1

CITED BY

Showing 1-39 of 39 citing papers · Page 1 of 1