Recommender systems in the tourism domain are gaining increasing attention, yet the development of diverse recommendation tasks remains limited, largely due to the scarcity of public datasets.This paper introduces Context Trails, a novel dataset addressing this gap.Context Trails distinguishes itself by including not only user interactions with touristic venues, but also the itineraries (trails or routes) followed by users.Furthermore, it enriches existing item features (e.g., category, coordinates) with contextual attributes related to the interaction moment (e.g., weather) and the venue itself (e.g., opening hours).Beyond a detailed description of the dataset's characteristics, we evaluate the performance of several baseline algorithms across three distinct recommendation tasks: classical recommendation, route recommendation, and contextual recommendation.We believe this dataset will foster further research and development of advanced recommender systems within the tourism domain.

Data sparsity is a persistent challenge in recommender systems, especially in specific domains like Point-of-Interest (POI) recommendation, where it significantly impacts model performance. While classical recommender systems have used various imputation and data augmentation mechanisms to address data sparsity, these methods have not been extensively explored in the POI recommendation domain. In this work, we propose a generic imputation framework to study the use of data augmentation techniques to generate synthetic check-ins and analyze their effects on the POI recommendation scenario. Our main goal is to enhance the performance of various traditional recommenders by increasing the training set interactions, considering specific characteristics of the domain, such as geographical information. We apply these techniques in six different cities from a global Foursquare check-in dataset, as well as in two additional cities from the Gowalla dataset, and a separate dataset from Yelp, ensuring a comprehensive evaluation across multiple data sources. Our imputation approach evidences improvements for most models. In several cases, these improvements exceeded 100% for ranking accuracy, measured in terms of nDCG, without considerably compromising novelty or diversity. Data and code are released at https://github.com/pablosanchezp/ImputationForPOIRecsys .

Abstract Location-Based Social Networks stimulated the rise of services such as Location-based Recommender Systems. These systems suggest to users points of interest (or venues) to visit when they arrive in a specific city or region. These recommendations impact various stakeholders in society, like the users who receive the recommendations and venue owners. Hence, if a recommender generates biased or polarized results, this affects in tangible ways both the experience of the users and the providers’ activities. In this paper, we focus on four forms of polarization, namely venue popularity, category popularity, venue exposure, and geographical distance. We characterize them on different families of recommendation algorithms when using a realistic (temporal-aware) offline evaluation methodology while assessing their existence. Besides, we propose two automatic approaches to mitigate those biases. Experimental results on real-world data show that these approaches are able to jointly improve the recommendation effectiveness, while alleviating these multiple polarizations.

The involvement of geographic information differentiates point-of-interest recommendation from traditional product recommendation. This geographic influence is usually manifested in the effect of users tending toward visiting nearby locations, but further mobility patterns can be used to model different groups of users. In this study, we characterize the check-in behavior of local and traveling users in a global Foursquare check-in data set. Based on the features that capture the mobility and preferences of the users, we obtain representative groups of travelers and locals through an independent cluster analysis. Interestingly, for locals, the mobility features analyzed in this work seem to aggravate the cluster quality, whereas these signals are fundamental in defining the traveler clusters. To measure the effect of such a cluster analysis when categorizing users, we compare the performance of a set of recommendation algorithms, first on all users together, and then on each user group separately in terms of ranking accuracy, novelty, and diversity. Our results on the Foursquare data set of 139,270 users in five cities show that locals, despite being the most numerous groups of users, tend to obtain lower values than the travelers in terms of ranking accuracy while these locals also seem to receive more novel and diverse POI recommendations. For travelers, we observe the advantages of popularity-based recommendation algorithms in terms of ranking accuracy, by recommending venues related to transportation and large commercial establishments. However, there are huge differences in the respective travelers groups, especially between predominantly domestic and international travelers. Due to the large influence of mobility on the recommendations, this article underlines the importance of analyzing user groups differently when making and evaluating personalized point-of-interest recommendations.

Artículos en revistas

Users from Location-Based Social Networks can be characterised by how and where they move. However, most of the works that exploit this type of information neglect either its sequential or its geographical properties. In this article, we focus on a specific family of recommender systems, those based on nearest neighbours; we define related users based on common check-ins and similar trajectories and analyse their effects on the recommendations. For this purpose, we use a real-world dataset and compare the performance on different dimensions against several state-of-the-art algorithms. The results show that better neighbours could be discovered with these approaches if we want to promote novel and diverse recommendations.

Research in Recommender Systems evaluation remains critical to study the efficiency of developed algorithms. Even if different aspects have been addressed and some of its shortcomings - such as biases, robustness, or cold start - have been analyzed and solutions or guidelines have been proposed, there are still some gaps that need to be further investigated. At the same time, the increasing amount of data collected by most recommender systems allows to gather valuable information from users and items which is being neglected by classical offline evaluation metrics. In this work, we integrate such information into the evaluation process in two complementary ways: on the one hand, we aggregate any evaluation metric according to the groups defined by the user attributes, and, on the other hand, we exploit item attributes to consider some recommended items as surrogates of those interacted by the user, with a proper penalization. Our results evidence that this novel evaluation methodology allows to capture different nuances of the algorithms performance, inherent biases in the data, and even fairness of the recommendations.

The use of contextual information like geographic, temporal (including sequential), and item features in Recommender Systems has favored their development in several different domains such as music, news, or tourism, together with new ways of evaluating the generated suggestions. This paper presents the underlying research in a PhD thesis introducing some of the fundamental considerations of the current tourism-based models, emphasizing the Point-Of-Interest (POI) problem, while proposing solutions using some of these additional contexts to analyze how the recommendations are made and how to enrich them. At the same time, we also intend to redefine some of the traditional evaluation metrics using contextual information to take into consideration other complementary aspects beyond item relevance. Our preliminary results show that there is a noticeable popularity bias in the POI recommendation domain that has not been studied in detail so far; moreover, the use of contextual information (such as temporal or geographical) help us both to improve the performance of recommenders and to get better insights of the quality of provided suggestions.

Typically, performance of recommender systems has been measured focusing on the amount of relevant items recommended to the users. However, this perspective provides an incomplete view of an algorithm's quality, since it neglects the amount of negative recommendations by equating the unknown and negatively interacted items when computing ranking-based evaluation metrics. In this paper, we propose an evaluation framework where anti-relevance is seamlessly introduced in several ranking-based metrics; in this way, we obtain a different perspective on how recommenders behave and the type of suggestions they make. Based on our results, we observe that non-personalized approaches tend to return less bad recommendations than personalized ones, however the amount of unknown recommendations is also larger, which explains why the latter tend to suggest more relevant items. Our metrics based on anti-relevance also show the potential to discriminate between algorithms whose performance is very similar in terms of relevance.