Deep Potential Geo-Social Relationship Mining for Point-of-Interest Recommendation

Pan, Zhenggao; Lin, Chia-Wen; Zhang, Zhiwei; Li, Xianwei; Chen, Guolong

doi:10.1109/access.2019.2930311

Cited by 13 publications

(9 citation statements)

References 34 publications

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“…F is the friend set and i F denotes all friend set of user i u . Then, the problem of POI recommendation is transformed into predicting the unvisited POIs in P and recommending them to user i u [2]. To better depict the proposed DDR-PR model, five core concepts are defined as follows.…”

Section: Related Workmentioning

confidence: 99%

“…In our research work, we adopt the existed two real-world datasets crawled from Foursquare [2] and Yelp [13], respectively. The checking-in records in the datasets contain users' ID, users' check-in locations, users' social relationships, and location details, etc.…”

Section: Experimental Datasets and Experimental Settingmentioning

confidence: 99%

“…In recent years, Web location acquisition technologies have developed rapidly, and some location-based social networks (LBSNs) such as Foursquare, Yelp, Gowalla have attracted many users [1][2][3]. Under these LBSNs, users usually check-in many POIs and leave their experiences on visiting these POIs.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fusing Dual Geo-Social Relationship and Deep Implicit Interest Topic Similarity for POI Recommendation

Cui¹,

Cui²,

Wang³

et al. 2022

Journal of Internet Technology

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<p>Nowadays, POI recommendation has been a hot research area, which are almost based on incomplete social relationships and geographical influence. However, few research simultaneously focuses on the refined social relationship and the user deep implicit topic similarity under a reachable region. Under this background, a novel Dual Geo-Social Relationship and Deep Implicit Interest Topic Similarity mining under a Reachable Region for POI Recommendation (DDR-PR) is proposed. DDR-PR first adopts kernel density estimation to compute the user checking-in reachable area. Under the reachable area, the combined relationship similarity based on the link relationship and common check-in social relationship is computed out. Then, the deep implicit interest topic similarity between users is mined out adopting the proposed topic model RTAU-TCP. We formulate the combined relationship similarity and implicit interest topic similarity as two regularization terms to incorporate into matrix factorization, which can recommend new POIs for a user under his or her reachable area. Extensive experiments prove the superiority of DDR-PR.</p> <p> </p>

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Section: Related Workmentioning

confidence: 99%

Section: Experimental Datasets and Experimental Settingmentioning

confidence: 99%

See 1 more Smart Citation

Fusing Dual Geo-Social Relationship and Deep Implicit Interest Topic Similarity for POI Recommendation

Cui¹,

Cui²,

Wang³

et al. 2022

Journal of Internet Technology

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“…The method proposed in [13] injects the user's Geo-social preferences into the POI recommendation process and leverages a three-level joint pairwise ranking model to reconstruct the POI recommendation model. Pan et al [14] introduce a POI recommendation model by the deep potential Geo-social relations. With the help of two-hop random walk and Jaccard similarity coefficient, the explicit and invisible Geo-social relationship is considered at the same time to provide the recommendation of POI.…”

Section: A Poi Relevance Evaluationmentioning

confidence: 99%

A Diverse and Personalized POI Recommendation Approach by Integrating Geo-Social Embedding Relations

Meng

Fang

2020

IEEE Access

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“…Markov Chain based stochastic models have been explored extensively in this regard [1,2,3,4,5,6,7,8,9]. Due to the success of Matrix Factorization (MF [10]) based methods for recommendation systems in other domains, MF methods [11,12,13,2,14,15,16,17] have also been studied for better POI recommendation modeling. To achieve better performance than vanilla MF methods, Bayesian Personalized Ranking (BPR [18]) methods have been employed [19,20,21,22,23,24,25,8].…”

Section: Introductionmentioning

confidence: 99%

A Survey on Deep Learning Based Point-Of-Interest (POI) Recommendations

Islam¹,

Mohammad²,

Das³

et al. 2020

Preprint

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Location-based Social Networks (LBSNs) enable users to socialize with friends and acquaintances by sharing their check-ins, opinions, photos, and reviews. Huge volume of data generated from LBSNs opens up a new avenue of research that gives birth to a new sub-field of recommendation systems, known as Point-of-Interest (POI) recommendation. A POI recommendation technique essentially exploits users' historical check-ins and other multi-modal information such as POI attributes and friendship network, to recommend the next set of POIs suitable for a user. A plethora of earlier works focused on traditional machine learning techniques by using hand-crafted features from the dataset. With the recent surge of deep learning research, we have witnessed a large variety of POI recommendation works utilizing different deep learning paradigms. These techniques largely vary in problem formulations, proposed techniques, used datasets and features, etc. To the best of our knowledge, this work is the first comprehensive survey of all major deep learning-based POI recommendation works. Our work categorizes and critically analyzes the recent POI recommendation works based on different deep learning paradigms and other relevant features. This review can be considered a cookbook for researchers or practitioners working in the area of POI recommendation.

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Deep Potential Geo-Social Relationship Mining for Point-of-Interest Recommendation

Cited by 13 publications

References 34 publications

Fusing Dual Geo-Social Relationship and Deep Implicit Interest Topic Similarity for POI Recommendation

Fusing Dual Geo-Social Relationship and Deep Implicit Interest Topic Similarity for POI Recommendation

A Diverse and Personalized POI Recommendation Approach by Integrating Geo-Social Embedding Relations

A Survey on Deep Learning Based Point-Of-Interest (POI) Recommendations

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