Geo-Pairwise Ranking Matrix Factorization Model for Point-of-Interest Recommendation

Zhao, Shenglin; King, Irwin; Lyu, Michael R.

doi:10.1007/978-3-319-70139-4_37

Cited by 11 publications

(6 citation statements)

References 16 publications

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“…This method is used as a baseline in the latest work [ 46 ]. (2) The second one is WARP-MF [ 47 ]. This is a pairwise ranking method that utilizes matrix factorization to minimize the basic WARP loss.…”

Section: Methodsmentioning

confidence: 99%

Pedestrian Flow Prediction and Route Recommendation with Business Events

Song

Zheng

et al. 2022

Sensors

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Due to the potential economic benefits, pedestrian flow is considered an essential indication of public spaces. Pedestrian flow prediction is designed to assist operators in making decisions (such as shopping center owners). Operators hold certain events, such as sales promotions, to attract surrounding pedestrians; we refer to this type of event as a business event. Business events attract pedestrian flows, which means business opportunities for the merchants. Moreover, their placement will affect the distributions of the pedestrian flows. However, deciding which route is chosen for a specified event is difficult. To the best of our knowledge, we are the first to consider business events when predicting pedestrian flow. In this paper, we investigate two problems: one is pedestrian flow prediction with business events, and the other is route recommendation for business events. First, we propose an Attraction-Based Matrix Factorization model (ABMF) to efficiently predict the pedestrian flow with business events, which introduces the attraction index of different categories to pedestrians in matrix factorization. Second, we leverage the Skip-gram mode to learn the latent representations and improve the pair-wise ranking loss to a flow-aware-based method (SG-FWARP), which aims to learn events’ latent representations for route recommendation. Compared with other state-of-the-art methods, the experimental results show ABMF can predict pedestrian flow matrix with a similarity of over 0.9 compared with the ground truth, and SG-FWARP can recommend routes for business events with high accuracy.

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Section: Methodsmentioning

confidence: 99%

Pedestrian Flow Prediction and Route Recommendation with Business Events

Song

Zheng

et al. 2022

Sensors

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“…According to Zhao et al (2017), geographical information influences POIs recommendation because users' activities in LBSNs are limited by location constraints. Therefore, several works have combined social relationships and geographical coordinates obtained from LBSNs to improve POIs recommendation (Zhao et al, 2017;Guo et al, 2019;Gao et al, 2019;Yang et al, 2019;Christoforidis et al, 2019;Lyu et al, 2020;Ma et al, 2020;Wei and Zhang, 2020;Liu et al, 2020).…”

Section: Related Workmentioning

confidence: 99%

“…On the other hand, Zhao et al (2017) proposed a novel collaborative filtering method that incorporates geographical influence to tackle the problem of geographically noisy POIs. Similarly, Gao et al (2018) proposed a POI recommendation approach using a Gaussian radial basis kernel function based support vector regression (SVR) model to predict geographical influence among users, and, then, devise a novel trust-based recommendation model to simultaneously incorporate both explicit and implicit social trust information into the process of POI recommendation.…”

Section: Related Workmentioning

confidence: 99%

FACF: fuzzy areas-based collaborative filtering for point-of-interest recommendation

Tourinho

Rios

2021

IJCSE

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Several online social networks collect information from their users' interactions (co-tagging of photos, co-rating of products, etc.) producing a large amount of activity-based data. As a consequence, this kind of information is used by these social networks to provide their users with recommendations about new products or friends. Moreover, recommendation systems (RS) become capable to predict a person's activity with no special infrastructure or hardware, such as RFID tags, or by using video and audio. In that sense, we propose a technique to provide personalised points-of-interest (POIs) recommendations for users of location-based social networks (LBSNs). Our technique assumes users' preferences can be characterised by their visited locations, which is shared by them on LBSN, collaboratively exposing important features as, for instance, areas-of-interest (AOIs) and POIs popularity. Therefore, our technique, named fuzzy areas-based collaborative filtering, uses users' activities to model their preferences and recommend the next visits to them. We have performed experiments over two real LBSN datasets and the obtained results have shown our technique outperforms location collaborative filtering at almost all of the experimental evaluation. Therefore, by fuzzy clustering of AOIs, FACF is suitable to check the popularity of POIs to improve POIs recommendation.

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“…Li et al [5] investigated the POI recommendation by treating it as the ordered weighted pairwise classification problem and proposed a ranking-based geographical factorization model. Zhao et al [48] first proposed the cogeographical influence to filter geographically noisy POIs and then incorporated it into a personalized pair-wise preference ranking matrix factorization model. Although ranking-based POI recommendations have been well studied, most of them do not consider incorporating these contexts by utilizing the embedding techniques and cannot be directly applied to the cold-start situations.…”

Section: Implicit Feedback-based Recommendationmentioning

confidence: 99%

Network Embedding‐Aware Point‐of‐Interest Recommendation in Location‐Based Social Networks

et al. 2019

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As one of the important techniques to explore unknown places for users, the methods that are proposed for point-of-interest (POI) recommendation have been widely studied in recent years. Compared with traditional recommendation problems, POI recommendations are suffering from more challenges, such as the cold-start and one-class collaborative filtering problems. Many existing studies have focused on how to overcome these challenges by exploiting different types of contexts (e.g., social and geographical information). However, most of these methods only model these contexts as regularization terms, and the deep information hidden in the network structure has not been fully exploited. On the other hand, neural network-based embedding methods have shown its power in many recommendation tasks with its ability to extract high-level representations from raw data. According to the above observations, to well utilize the network information, a neural network-based embedding method (node2vec) is first exploited to learn the user and POI representations from a social network and a predefined location network, respectively. To deal with the implicit feedback, a pair-wise ranking-based method is then introduced. Finally, by regarding the pretrained network representations as the priors of the latent feature factors, an embedding-based POI recommendation method is proposed. As this method consists of an embedding model and a collaborative filtering model, when the training data are absent, the predictions will mainly be generated by the extracted embeddings. In other cases, this method will learn the user and POI factors from these two components. Experiments on two real-world datasets demonstrate the importance of the network embeddings and the effectiveness of our proposed method.

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Geo-Pairwise Ranking Matrix Factorization Model for Point-of-Interest Recommendation

Cited by 11 publications

References 16 publications

Pedestrian Flow Prediction and Route Recommendation with Business Events

Pedestrian Flow Prediction and Route Recommendation with Business Events

FACF: fuzzy areas-based collaborative filtering for point-of-interest recommendation

Network Embedding‐Aware Point‐of‐Interest Recommendation in Location‐Based Social Networks

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