Point-of-Interest Recommender Systems based on Location-Based Social Networks: A Survey from an Experimental Perspective

Sánchez, Pablo; Bellogín, Alejandro

doi:10.48550/arxiv.2106.10069

Cited by 2 publications

(2 citation statements)

References 110 publications

(186 reference statements)

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“…The measure nDCG@N evaluates the ranking quality of the recommendation models. We follow the common practice from previous works [5,47,53] to evaluate the proposed model and baselines, we split the Gowalla and Yelp datasets into three different parts for each user, i.e., train, valid, and test sets. To this end, we sort the check-ins of each user in chronological order and take the 20% most recent check-ins as the test set, the next 10% as the validation set, and the rest as the training set.…”

Section: Evaluation Metricsmentioning

confidence: 99%

Leveraging Social Influence based on Users Activity Centers for Point-of-Interest Recommendation

Seyedhoseinzadeh¹,

Rahmani²,

Afsharchi³

et al. 2022

Preprint

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Recommender Systems (RSs) aim to model and predict the user preference while interacting with items, such as Points of Interest (POIs). These systems face several challenges, such as data sparsity, limiting their effectiveness. In this paper, we address this problem by incorporating social, geographical, and temporal information into the Matrix Factorization (MF) technique. To this end, we model social influence based on two factors: similarities between users in terms of common check-ins and the friendships between them. We introduce two levels of friendship based on explicit friendship networks and high check-in overlap between users. We base our friendship algorithm on users' geographical activity centers. The results show that our proposed model outperforms the state-of-the-art on two real-world datasets. More specifically, our ablation study shows that the social model improves the performance of our proposed POI recommendation system by 31% and 14% on the Gowalla and Yelp datasets in terms of Precision@10, respectively.

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Section: Evaluation Metricsmentioning

confidence: 99%

Leveraging Social Influence based on Users Activity Centers for Point-of-Interest Recommendation

Seyedhoseinzadeh¹,

Rahmani²,

Afsharchi³

et al. 2022

Preprint

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“…Point-of-Interest (POI) recommendation is an essential service to location-based social networks (LBSNs). Providing personalized POI systems ease the inevitable problem of information overload on users and helps businesses attract potential customers [33,30]. However, as a highly data-driven system, these systems could be impacted by data or algorithmic bias, providing unfair outcomes and weakening the system's trustworthiness.…”

Section: Introductionmentioning

confidence: 99%

The Unfairness of Active Users and Popularity Bias in Point-of-Interest Recommendation

Rahmani¹,

Deldjoo²,

Tourani³

et al. 2022

Preprint

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Point-of-Interest (POI) recommender systems provide personalized recommendations to users and help businesses attract potential customers. Despite their success, recent studies suggest that highly datadriven recommendations could be impacted by data biases, resulting in unfair outcomes for different stakeholders, mainly consumers (users) and providers (items). Most existing fairness-related research works in recommender systems treat user fairness and item fairness issues individually, disregarding that RS work in a two-sided marketplace. This paper studies the interplay between (i) the unfairness of active users, (ii) the unfairness of popular items, and (iii) the accuracy (personalization) of recommendation as three angles of our study triangle. We group users into advantaged and disadvantaged levels to measure user fairness based on their activity level. For item fairness, we divide items into short-head, mid-tail, and long-tail groups and study the exposure of these item groups into the top-k recommendation list of users. Experimental validation of eight different recommendation models commonly used for POI recommendation (e.g., contextual, CF) on two publicly available POI recommendation datasets, Gowalla and Yelp, indicate that most well-performing models suffer seriously from the unfairness of popularity bias (provider unfairness). Furthermore, our study shows that most recommendation models cannot satisfy both consumer and producer fairness, indicating a trade-off between these variables possibly due to natural biases in data. We choose the POI recommendation as our test scenario; however, the insights should be trivially extendable on other domains.

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Point-of-Interest Recommender Systems based on Location-Based Social Networks: A Survey from an Experimental Perspective

Cited by 2 publications

References 110 publications

Leveraging Social Influence based on Users Activity Centers for Point-of-Interest Recommendation

Leveraging Social Influence based on Users Activity Centers for Point-of-Interest Recommendation

The Unfairness of Active Users and Popularity Bias in Point-of-Interest Recommendation

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