A reproducible POI recommendation framework: Works mapping and benchmark evaluation

Werneck, Heitor; Silva, Nícollas; Pereira, Adriano C. M.; Carvalho, Matheus Carvalho de; Bellogín, Alejandro; Martinez‐Gil, Jorge; Mourão, Fernando; Rocha, Leonardo

doi:10.1016/j.is.2022.102019

Cited by 8 publications

(4 citation statements)

References 7 publications

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“…In contrast with other existing open-source libraries, DaisyRec aims to rigorously evaluate the performance of the recommendation. 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%

“…Previous research works such as [12] have shown the way incorporating these rich contexts information have a significant impact on the performance of POI recommendation models. Therefore, in recent years, there has been a growth in the demand for specialized recommendation algorithms and methodologies that can incorporate and fuse contextual information into the POI recommendation process [24]; arXiv:2306.11395v1 [cs.IR] 20 Jun 2023…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Related Frameworkmentioning

confidence: 99%

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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“…How to quickly fnd resources which match users' needs from massive data has become a hot topic. To provide users with the most appropriate resources, recommender systems have been popular in various scenarios, such as news recommendations [1,2] POI location recommendation [3,4], recommendation of goods [5], and learning resource recommendation [6].…”

Section: Introductionmentioning

confidence: 99%

A Recommendation Approach Based on Heterogeneous Network and Dynamic Knowledge Graph

Wan,

Wu,

Liu

et al. 2024

International Journal of Intelligent Systems

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Besides data sparsity and cold start, recommender systems often face the problems of selection bias and exposure bias. These problems influence the accuracy of recommendations and easily lead to overrecommendations. This paper proposes a recommendation approach based on heterogeneous network and dynamic knowledge graph (HN-DKG). The main steps include (1) determining the implicit preferences of users according to user’s cross-domain and cross-platform behaviors to form multimodal nodes and then building a heterogeneous knowledge graph; (2) Applying an improved multihead attention mechanism of the graph attention network (GAT) to realize the relationship enhancement of multimodal nodes and constructing a dynamic knowledge graph; and (3) Leveraging RippleNet to discover user’s layered potential interests and rating candidate items. In which, some mechanisms, such as user seed clusters, propagation blocking, and random seed mechanisms, are designed to obtain more accurate and diverse recommendations. In this paper, the public datasets are used to evaluate the performance of algorithms, and the experimental results show that the proposed method has good performance in the effectiveness and diversity of recommendations. On the MovieLens-1M dataset, the proposed model is 18%, 9%, and 2% higher than KGAT on F1, NDCG@10, and AUC and 20%, 2%, and 0.9% higher than RippleNet, respectively. On the Amazon Book dataset, the proposed model is 12%, 3%, and 2.5% higher than NFM on F1, NDCG@10, and AUC and 0.8%, 2.3%, and 0.35% higher than RippleNet, respectively.

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“…This recommendation method not only allows consumers to quickly find their interested locations from massive data, but also helps some entity enterprises to improve the existing POI services or develop new POIs [2], so as to obtain more benefits. Therefore, POI recommendation has become an emergent subfield of recommender systems [3,4].…”

Section: Introductionmentioning

confidence: 99%

Point of Interest Recommendation Method Based on Bidirectional Matrix and Deep Belief Network

Song

et al. 2023

IEEJ Transactions Elec Engng

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Point‐of‐interest (POI) recommendation has always faced serious data sparsity problem. Most of the existing POI recommendation methods do not consider the bidirectional relationship between the user‐preference for locations and the location‐ attractiveness to users in the original dataset when solving the sparsity. For this problem, a point‐of‐interest recommendation method based on bidirectional matrix and deep belief network (POI_DBNBM) is proposed in this paper. Specially, from the perspectives of users and locations, we first construct two different matrices with bidirectional relationship by mapping the original dataset, and we use deep belief network (DBN) with strong learning ability to predict the two matrices. Then, a local–global weight optimization method is developed to weight and fuse the two result matrices obtained by DBN, so as to achieve the more precise user's rating prediction matrix for locations. The abundant experimental studies and evaluations are conducted on FourSquare dataset. Results are encouraging and better than those previously reported on the dataset. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

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A reproducible POI recommendation framework: Works mapping and benchmark evaluation

Cited by 8 publications

References 7 publications

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

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

A Recommendation Approach Based on Heterogeneous Network and Dynamic Knowledge Graph

Point of Interest Recommendation Method Based on Bidirectional Matrix and Deep Belief Network

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