An adaptive point-of-interest recommendation method for location-based social networks based on user activity and spatial features

Si, Yali; Zhang, Fuzhi; Liu, Wenyuan

doi:10.1016/j.knosys.2018.08.031

Cited by 62 publications

(20 citation statements)

References 28 publications

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“…It however made the data more sparse by simply dividing the check-in records according to these features. In [14], Si et al presented an adaptive POI recommendation approach, which extracts three-dimensional user activity, time-based POI popularity, and distance features using a probabilistic statistical analysis method from historical check-in datasets on LBSNs. Unfortunately, it ignores the fact that the popularity of POIs are not only related to the time.…”

Section: Related Workmentioning

confidence: 99%

Context-Specific Point-of-Interest Recommendation Based on Popularity-Weighted Random Sampling and Factorization Machine

Shen

et al. 2021

IJGI

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Point-Of-Interest (POI) recommendation not only assists users to find their preferred places, but also helps businesses to attract potential customers. Recent studies have proposed many approaches to the POI recommendation. However, the lack of negative samples and the complexities of check-in contexts limit their effectiveness significantly. This paper focuses on the problem of context-specific POI recommendation based on the check-in behaviors recorded by Location-Based Social Network (LBSN) services, which aims at recommending a list of POIs for a user to visit at a given context (such as time and weather). Specifically, a bidirectional influence correlativity metric is proposed to measure the semantic feature of user check-in behavior, and a contextual smoothing method to effectively alleviate the problem of data sparsity. In addition, the check-in probability is computed based on the geographical distance between the user’s home and the POI. Furthermore, to handle the problem of no negative feedback in LBSN, a weighted random sampling method is proposed based on contextual popularity. Finally, the recommendation results is obtained by utilizing Factorization Machine with Bayesian Personalized Ranking (BPR) loss. Experiments on a real dataset collected from Foursquare show that the proposed approach has better performance than others.

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Section: Related Workmentioning

confidence: 99%

Context-Specific Point-of-Interest Recommendation Based on Popularity-Weighted Random Sampling and Factorization Machine

Shen

et al. 2021

IJGI

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“…In the POI recommendation system, the user's check-in record has only positive samples, there is lack of negative sample information, and the amount of data is sparse. Therefore, recommending a location is challenging [13]. The early POI recommendation was to recommend the most popular Top-K points of interest to users.…”

Section: Related Researchmentioning

confidence: 99%

POI Recommendation Method Using Deep Learning in Location-Based Social Networks

Liu

2021

Wireless Communications and Mobile Computing

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To solve the problems of large data sparsity and lack of negative samples in most point of interest (POI) recommendation methods, a POI recommendation method based on deep learning in location-based social networks is proposed. Firstly, a bidirectional long-short-term memory (Bi-LSTM) attention mechanism is designed to give different weights to different parts of the current sequence according to users’ long-term and short-term preferences. Then, the POI recommendation model is constructed, the sequence state data of the encoder is input into Bi-LSTM-Attention to get the attention representation of the current POI check-in sequence, and the Top- N recommendation list is generated after the decoder processing. Finally, a negative sampling method is proposed to obtain an effective negative sample set, which is used to improve the calculation of the Bayesian personalized ranking loss function. The proposed method is demonstrated experimentally on Foursquare and Gowalla datasets. The experimental results show that the proposed method has better accuracy, recall, and F1 value than other comparison methods.

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“…This model categorized friends as neighbors, local and social friends. An adaptive POI‐based algorithm was proposed using geographical and user activity influence which used two different strategies based on user activity online and incorporated those strategies with the popularity of location feature 27 . To reduce the risk of a statistical inference attack an intelligent pseudo‐location recommendation framework 28 has been introduced in location recommendation system.…”

Section: Related Workmentioning

confidence: 99%

Location‐based social network recommendations with computational intelligence‐based similarity computation and user check‐in behavior

Elangovan

Subramaniyaswamy

Varadarajan

et al. 2020

Concurrency and Computation

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Location recommending frameworks plays a very significant role in suggesting the users with new places to visit especially when users are visiting unfamiliar areas. Most of the existing recommender systems do not consider the fact that different users have different behavior while checking in. Some approaches do not consider the essential factors while providing recommendations. These systems lack adaptability and hence they provide poor recommendations. An adaptive approach to provide users with a personalized recommendation has been proposed in this paper. We have considered three features namely, user activeness feature, user similarity feature, and the spatial feature. In addition to this, we have also considered the location popularity for a given timeslot. We have divided the users into inactive and active based on the degree of activeness on social networks using fuzzy c‐means clustering. We have provided two strategies based on the activeness of the user. A two‐dimensional Gaussian kernel density estimation strategy is used for the active user. A one‐dimensional power‐law function strategy is used for inactive users. Moreover, we have integrated the time‐based popularity of the location and probability estimation based on the similarity between the users. To evaluate the proposed model, we have used a large‐scale Foursquare dataset.

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An adaptive point-of-interest recommendation method for location-based social networks based on user activity and spatial features

Cited by 62 publications

References 28 publications

Context-Specific Point-of-Interest Recommendation Based on Popularity-Weighted Random Sampling and Factorization Machine

Context-Specific Point-of-Interest Recommendation Based on Popularity-Weighted Random Sampling and Factorization Machine

POI Recommendation Method Using Deep Learning in Location-Based Social Networks

Location‐based social network recommendations with computational intelligence‐based similarity computation and user check‐in behavior

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