A personalized point-of-interest recommendation model via fusion of geo-social information

Gao, Rong; Li, Jing; Li, Xuefei; Song, Chengfang; Zhou, Yifei

doi:10.1016/j.neucom.2017.08.020

Cited by 63 publications

(32 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, some geographical analysis models, including KDE [7,11,[16][17][18][19][20][21][22]33,34], the multi-center Gaussian model (MGM) [10,23] and the power-law distribution (PD) [15,24] are introduced in location recommendation. These models significantly improve the recommendation quality.…”

Section: Related Workmentioning

confidence: 99%

“…By contrast, non-parametric estimation (i.e., KDE) does not make any assumptions about the implied distribution form, and it learns the distribution form from the data. Zhang et al [16,[19][20][21][22] used a one-dimensional KDE (1D-KDE) model for geographical modeling; these methods learn the distance distribution from users' check-in history. Zhang et al [11,17,33,34] introduced a two-dimensional KDE (2D-KDE) model to determine the check-in probability distribution; 2D-KDE is more intuitive and reasonable than 1D-KDE.…”

Section: Related Workmentioning

confidence: 99%

“…Figure 2 shows the distributions of the locations in the three datasets. Crisp rules [7,16,26], fuzzy rules [47][48][49], machine learning and deep learning approaches [22,50], and hybrid approaches [51] are four major approaches for calculating the final score based on the various input contextual features. The sum rule belongs to the crisp rules, it is a simple and conventional fusion method.…”

Section: Dataset Descriptionmentioning

confidence: 99%

“…Thus, if a user visits a certain area most frequently, the check-in possibility of an unvisited location in this area is higher, and the possibility diminishes as the distance from the area increases. To better explore users' geographical distribution on locations, different geographical models have been proposed to model users' check-in behaviors at locations [7,10,11,[15][16][17][18][19][20][21][22][23][24]. For example, in [15], a power-law probabilistic model is proposed to capture the geographical influence among point-of-interests (POI), Liu et al [10] assumes that users' check-in behaviors follow a multi-center distribution, and both of these studies model the geographical distance distribution.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Exploiting Two-Dimensional Geographical and Synthetic Social Influences for Location Recommendation

Liu

Zhang

Liu

et al. 2020

IJGI

View full text Add to dashboard Cite

With the rapid development of location-based social networks (LBSNs), because human behaviors exhibit specific distribution patterns, personalized geo-social recommendation has played a significant role for LBSNs. In addition to user preference and social influence, geographical influence has also been widely researched in location recommendation. Kernel density estimation (KDE) is a key method in modeling geographical influence. However, most current studies based on KDE do not consider the problems of influence range and outliers on users’ check-in behaviors. In this paper, we propose a method to exploit geographical and synthetic social influences (GeSSo) on location recommendation. GeSSo uses a kernel estimation approach with a quartic kernel function to model geographical influences, and two kinds of weighted distance are adopted to calculate bandwidth. Furthermore, we consider the social closeness and connections between friends, and a synthetic friend-based recommendation method is introduced to model social influences. Finally, we adopt a sum framework which combines user’s preferences on a location with geographical and social influences. Extensive experiments are conducted on three real-life datasets. The results show that our method achieves superior performance compared to other advanced geo-social recommendation techniques.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Dataset Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Exploiting Two-Dimensional Geographical and Synthetic Social Influences for Location Recommendation

Liu

Zhang

Liu

et al. 2020

IJGI

View full text Add to dashboard Cite

show abstract

“…Moreover, when the data is too sparse, the latent feature representation of LDA model learning may not be very effective, and the performance is not satisfactory [ 31 – 33 ]. Most researches on recommendation systems based on machine learning use matrix decomposition technology to recommend items [ 34 ]. The method based on the matrix decomposition model is very sensitive to the initialization of the latent feature matrix of users and users' interest points [ 35 ].…”

Section: Introductionmentioning

confidence: 99%

Online Doctor Recommendation with Convolutional Neural Network and Sparse Inputs

Yan

2020

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

The recommendation system in the online medical consultation website is a system to assist patients to find appropriate doctors. Based on the analysis of the current situation of the development of an online medical community (Haodf.com) in China, this paper puts forward recommendation suggestions of finding the right hospital and doctor to promote the rapid integration of Internet technology and traditional medical services. A new recommendation model called Probabilistic Matrix Factorization integrated with Convolutional Neural Network (PMF-CNN) is proposed in the paper. Doctors’ data in Haodf.com were used to evaluate the performance of our system. The model improves the performance of medical consultation recommendations by fusing review text and doctor information based on CNN (Convolutional Neural Network). Specifically, CNN is used to learn the feature representation of the review text and the doctors’ information. Furthermore, the extended matrix factorization model is exploited to fuse the review information feature and the initial value of the doctors’ information for recommendation. As is shown in the experimental results on Haodf.com datasets, the proposed PMF-CNN achieves better recommendation performances than the other state-of-the-art recommendation algorithms. And the recommendation system in an online medical website improves the utilization efficiency of doctors and the balance of public health resources allocation.

show abstract

Geographical Proximity Boosted Recommendation Algorithms for Real Estate

Wang

Zhang

et al. 2018

Lecture Notes in Computer Science

View full text Add to dashboard Cite

A personalized point-of-interest recommendation model via fusion of geo-social information

Cited by 63 publications

References 35 publications

Exploiting Two-Dimensional Geographical and Synthetic Social Influences for Location Recommendation

Exploiting Two-Dimensional Geographical and Synthetic Social Influences for Location Recommendation

Online Doctor Recommendation with Convolutional Neural Network and Sparse Inputs

Geographical Proximity Boosted Recommendation Algorithms for Real Estate

Contact Info

Product

Resources

About