Friend and POI recommendation based on social trust cluster in location-based social networks

Zhu, Jinghua; Wang, Chao; Guo, Xu; Ming, Qian; Li, Jinbao; Liu, Yong

doi:10.1186/s13638-019-1388-2

Cited by 23 publications

(15 citation statements)

References 28 publications

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“…Analysis of data collected from LBSN platforms led DeScioli et al [13] to conclude that social connections are highly related to geographical distance. Zhu et al [14] clustered users based on social trust in formulating recommendations of potential friends. Chen and Li [15] developed a novel graph embedding method to find potential propagators and customers in LBSNs.…”

Section: Recommendation Systems Using Data Acquired From Lbsnsmentioning

confidence: 99%

“…Hence, we developed the Partners Finding Speed Up Algorithm to solve this problem. This algorithm reduces the computational load using the regressive SRS scores between candidate partners and q in Equation (14). The candidate partners are first arranged from smallest to largest according to their distance from query POI q.…”

Section: Partners Finding Speed Up Algorithmmentioning

confidence: 99%

“…Logesh et al [10] and Huang et al [11] similarly employed user preferences to recommend reasonable travel routes to users. Zheng et al [12], DeScioli et al [13], Zhu et al [14], and Chen and Li [15] utilized LBSN data to identify the opinion leaders that users may be influenced by in their decisionmaking. Clearly, the methods developed by these papers are unlikely to be suitable for the aims of this study.…”

Section: Introductionmentioning

confidence: 99%

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Joint Promotion Partner Recommendation Systems Using Data from Location-Based Social Networks

Chen

Huang

Chiu

et al. 2021

IJGI

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Joint promotion is a valuable business strategy that enables companies to attract more customers at lower operational cost. However, finding a suitable partner can be extremely difficult. Conventionally, one of the most common approaches is to conduct survey-based analysis; however, this method can be unreliable as well as time-consuming, considering that there are likely to be thousands of potential partners in a city. This article proposes a framework to recommend Joint Promotion Partners using location-based social networks (LBSN) data. We considered six factors in determining the suitability of a partner (customer base, association, rating and awareness, prices and star ratings, distance, and promotional strategy) and developed efficient algorithms to perform the required calculations. The effectiveness and efficiency of our algorithms were verified using the Foursquare dataset and real-life case studies.

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Section: Recommendation Systems Using Data Acquired From Lbsnsmentioning

confidence: 99%

Section: Partners Finding Speed Up Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Joint Promotion Partner Recommendation Systems Using Data from Location-Based Social Networks

Chen

Huang

Chiu

et al. 2021

IJGI

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“…The learned preferences are enhanced by the integration of the learning model with social influence. This work is similar to our work in the sense that it considers the notion of similar preferences among friends. Zhu et al (2019) proposed a trust prediction method based on identified trust clusters. The friends' recommendation is based on the trust value and the similarity among individuals.…”

Section: Related Workmentioning

confidence: 99%

Recommendation of users in social networks: A semantic and social based classification approach

et al. 2020

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Recently, the study of social network‐based recommender systems has become an active research topic. The integration of the social relationships that exist between users can improve the accuracy of recommendation results since the users' preferences are similar or influenced by their connected friends. We focus in this article on the recommendation of users in social networks. Our approach is based on semantic and social representations of the users' profiles. We have formalized and illustrated these two dimensions using the Yelp social network. The novelty of our approach concerns the modelling of the credibility of the user, through his/her trust and commitment in the social network. Moreover, in order to optimize the performance of the recommendation process, we have used two classification techniques: an unsupervised technique that uses the K‐means algorithm (applied initially to all users); and a supervised technique that uses the K‐Nearest Neighbours algorithm (applied to newly added users). A recommendation algorithm has been proposed taking into account the cold‐start and sparsity problems. A prototype of a recommender system has been developed and tested using two publicly available datasets: the Yelp database and the Rich Epinions database. The comparative evaluation results show the effectiveness of combining the semantic, the social and the credibility information in an approach that appropriately uses the K‐means and K‐Nearest Neighbours algorithms.

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“…To implement the proposed algorithm in this research, real-time and hybrid networks were used, and the results showed that the proposed algorithm has high accuracy for identifying communities and belonging to a node to a community. Zhu et al [35] focused on how various social networks, including Foursquare and Instagram, work. ese networks often use two methods, including Point-of-Interest (POI) discovery and friendly advice to make suggestions based on where people live.…”

mentioning

confidence: 99%

TCDABCF: A Trust-Based Community Detection Using Artificial Bee Colony by Feature Fusion

Peng

Rastgari

Navaei

et al. 2021

Mathematical Problems in Engineering

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Social network aims to extend a widespread framework to communicate users and find alike people with common features, easier and faster. As people usually experience in everyday life, social communication can be formed from common groups with almost identical properties. Detecting such groups or communities is a challenging task in various fields of social network analysis. Many researchers intend to develop algorithms that work effectively and efficiently on social networks. It is believed that the most influential user in a community that had been followed by similar users could be a central point of a community or cluster, and the similar user would be members of the community. Research studies tend to increase intracommunity similarity and decrease intercommunity similarity to improve the performance of the community detection methods by finding such influential users accurately. In this paper, a hybrid metaheuristic method is proposed. In the proposed method called trust-based community detection using artificial bee colony by feature fusion (TCDABCF), we use a fusion approach combined with artificial bee colony (ABC) to improve the accuracy of the community detection task. In this approach, not only the social features of users are considered but also the relationship of trust between users in a community is also calculated. So, the proposed method can lead to finding more precise clusters of similar users with influential users in the center of each cluster. The proposed method uses the artificial bee colony (ABC) to find the influential users and the relation of their followers accurately. We compare this algorithm with nine state-of-the-art methods on the Facebook dataset. Experimental results show that the proposed method has obtained values of 0.9662 and 0.9533 for NMI and accuracy, respectively, which has improved in comparison with state-of-the-art community detection methods.

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Friend and POI recommendation based on social trust cluster in location-based social networks

Cited by 23 publications

References 28 publications

Joint Promotion Partner Recommendation Systems Using Data from Location-Based Social Networks

Joint Promotion Partner Recommendation Systems Using Data from Location-Based Social Networks

Recommendation of users in social networks: A semantic and social based classification approach

TCDABCF: A Trust-Based Community Detection Using Artificial Bee Colony by Feature Fusion

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