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: this https URL.

Introduction: The increasing flood of information on the web creates a need for selecting content according to the end user's preferences. Today, recommender systems are deployed on most internet platforms and play an important role in everybody's daily digital routine, including e-commerce websites, social networks, music streaming, or hotel booking. Recommender systems have been investigated over more than thirty years BIBREF0. Over the years, many models and datasets in different domains and various sizes have been developed: movies BIBREF1, Amazon products BIBREF2, BIBREF3, or music BIBREF4. With the tremendous success of large deep learning-based recommender systems, in better capturing user-item interactions, the recommendation quality has been significantly improved BIBREF5. However, the increase in recommendation performance with deep learning-based models comes at the cost of large datasets. Most recent state-of-the-art models, such as BIBREF6, BIBREF7, or BIBREF8 necessitate large datasets (i.e., millions) to achieve high performance. In the hotel domain, only a few works have studied hotel recommendation, such as BIBREF9 or BIBREF10. Additionally, to the best of our knowledge, the largest publicly available hotel review dataset contains $870k$ samples BIBREF11. Unlike commonly used recommendation datasets, the hotel domain suffers from higher data sparsity and therefore, traditional collaborative-filtering approaches cannot be applied BIBREF10, BIBREF12, BIBREF13. Furthermore, rating a hotel is different than traditional products, because the whole experience lasts longer, and there are more facets to review BIBREF12. In contrast, we propose in this work HotelRec, a novel large-scale hotel recommendation dataset based on hotel reviews from TripAdvisor, and containing approximately 50 million reviews. A sample review is shown in Figure FIGREF1. To the best of our knowledge, HotelRec is the largest publicly available hotel review dataset (at least 60 times larger than previous datasets). Furthermore, we analyze various aspects of the HotelRec dataset and benchmark the performance of different models on two tasks: rating prediction and recommendation performance. Although reasonable performance is achieved by a state-of-the-art method, there is still room for improvement. We believe that HotelRec will offer opportunities to apply and develop new large recommender systems, and push furthermore the recommendation for hotels, which differs from traditional datasets.

Question: How did they obtain the dataset?
Answer: The authors crawled all areas listed an TripAdvisor's SiteIndex and gathered all links related to hotels. Using Selenium, they put a time gap between opening each page, to mimic human behaviour and avoid having their scraper being detected. They discarded pages without a review and for pages with a review, they collected the review's profile, the overall rating, the summary, the written text and subratings, where given.