Points of Interest recommendations: Methods, evaluation, and future directions

Werneck, Heitor; Silva, Nícollas; Viana, Matheus Carvalho; Pereira, Adriano C. M.; Mourão, Fernando; Rocha, Leonardo

doi:10.1016/j.is.2021.101789

Cited by 22 publications

(16 citation statements)

References 65 publications

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“…Existing POI datasets. POIs are an important component of tourist visits and their recommendation has received strong attention from the research community [10,52]. A recent review discusses POI recommendation based on multimedia content [10], underlining the central role of visual content in recording tourist visits.…”

Section: Related Workmentioning

confidence: 99%

“…Such personalization can be achieved by leveraging user profiles that encode their tourist prefer-Figure 1: Proposed use-case of Vis2Rec . ences [10,52]. Mainstream recommendation methods relied on a form of matrix factorization to propose personalized content [21,14,44], while more recent methods use deep learning algorithms to improve the performances of recommender systems [9,43], and their effectiveness is largely determined by the quality and richness of the available profiles.…”

Section: Introductionmentioning

confidence: 99%

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Vis2Rec: A Large-Scale Visual Dataset for Visit Recommendation

Soumm¹,

Popescu²,

Delezoide

2023

2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

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Most recommendation datasets for tourism are restricted to one world region and rely on explicit data such as checkins. However, in reality, tourists visit various places worldwide and document their trips primarily through photos. These images contain a wealth of raw information that can be used to capture users' preferences and recommend personalized content. Visual content was already used in past works, but no large-scale publicly-available dataset that gives access to users' personal images exists for recommender systems. As such a resource would open-up possibilities for new image-based recommendation algorithms, we introduce Vis2Rec, a new dataset based on visit data extracted from users' Flickr photographic streams, which includes over 7 million photos, 36k recognizable points of interest, and 14k user profiles. Google Landmarks v2 is used as an auxiliary dataset to identify points of interest in users' photos, using a state-of-the-art image-matching deep architecture. Image-based user profiles are then constituted by aggregating the points of interest detected for each user. In addition, ground truth visits were determined for the test subset in order to enable accurate evaluation. Finally, we benchmark Vis2Rec using various existing recommender systems, and discuss the possibilities opened up by the availability of user images, as well as the societal issues that come with them. Following good practice in dataset sharing, Vis2Rec is created using only freely distributable content, and additional anonymization is performed to ensure the privacy of users. The raw dataset and the preprocessed user profiles will be publicly available at https://github.com/MSoumm/Vis2Rec.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Vis2Rec: A Large-Scale Visual Dataset for Visit Recommendation

Soumm¹,

Popescu²,

Delezoide

2023

2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

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“…Similar to CAPRI, Werneck et al [24] introduces an additional framework for 2 https://caprirecsys.github.io/CAPRI/ the reproducibility of POI experiment recommendations. However, their approach is not exhaustive and is not easily replicable, as it only generates the outcomes of their earlier work [23].…”

Section: Related Frameworkmentioning

confidence: 99%

“…Accordingly, the density of POI check-ins data is typically approximately 0.1%, whereas the density of Netflix data for movie suggestions is 1.2%. This is because a person can only visit a limited number of locations, whereas a city can contain a vast number of POIs; ‚ Necessity for multi-dimensional evaluation: Previous papers [8,18,23] in the POI field predominantly focus on accuracy-oriented metrics. However, there is a remarkable consensus in the RS community that there are other important facets to the recommendation process that accuracy metric systems cannot simply capture, such as the novelty, diversity, and catalog coverage of recommenders.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Impact of Temporal Bias in Point-of-Interest Recommendation

Rahmani

Naghiaei

Tourani

et al. 2022

Proceedings of the 16th ACM Conference on Recommender Systems

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Point-of-Interest (POI) recommendation systems have gained popularity for their unique ability to suggest geographical destinations, with the incorporation of contextual information such as time, location, and user-item interaction. Existing recommendation frameworks lack the contextual fusion required for POI systems. This paper presents CAPRI, a novel POI recommendation framework that effectively integrates context-aware models, such as GeoSoCa, LORE, and USG, and introduces a novel strategy for the efficient merging of contextual information. CAPRI integrates an evaluation module that expands the evaluation scope beyond accuracy to include novelty, personalization, diversity, and fairness. With an aim to establish a new industry standard for reproducible results in the realm of POI recommendation systems, we have made CAPRI openly accessible on GitHub, facilitating easy access and contribution to the continued development and refinement of this innovative framework. 1

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“…The weights are aggregated and the features of the entity are merged. There are two main meanings of adopting this method [5,6]: first, the calculation of the feature vector of the entity contained in the knowledge graph is weighted and aggregated with the information of the neighboring entity within a certain range of the entity, second, the information of the neighbor node is calculated for the node. The degree of selection is jointly influenced by the established entity and neighboring entities.…”

mentioning

confidence: 99%

Recommendation method for fusion of knowledge graph convolutional network

Jiang

Dong

2022

EURASIP J. Adv. Signal Process.

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In the application of internet of vehicles system, it is particularly important to obtain real-time and effective vehicle information and provide personalized functional services for vehicle operation. This algorithm combines knowledge graph technology with convolutional network and presents a new algorithm model, that is, when calculating the representation of a given entity in the knowledge graph, the information of the neighboring entity is combined with the deviation. Through the integration of neighbor entity information, the local neighborhood structure can be better captured and stored in each entity, and the weight of different neighbor entities depends on the relationship between them and the specific user, which can better reflect the user's personalized interests, in order to fully demonstrate the characteristics of the entity. Compared with the traditional coordinated filtering technology SVD model, this model has improved accuracy and F1 value.

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Points of Interest recommendations: Methods, evaluation, and future directions

Cited by 22 publications

References 65 publications

Vis2Rec: A Large-Scale Visual Dataset for Visit Recommendation

Vis2Rec: A Large-Scale Visual Dataset for Visit Recommendation

Exploring the Impact of Temporal Bias in Point-of-Interest Recommendation

Recommendation method for fusion of knowledge graph convolutional network

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