Knowledge Graph-Based Spatial-Aware User Community Preference Query Algorithm for LBSNs

Wang, Yanjun; Zhu, Liang; Ma, Jiangtao; Hu, Guangwu; Liu, Jiangchuan; Qiao, Yuanhua

doi:10.1016/j.bdr.2020.100169

Cited by 12 publications

(4 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Because existing research methods have not completely solved the problem of variable clustered nearest neighbor query (F ANNR ) in a road network, Chen et al [12] proposed a kF ANNR query based on keyword aware variant on this basis, but this query does not fully consider the preference of each user. In order to solve the problem of user community spatial preference and improve its performance, Wang et al [13] proposed a spatially aware user community preference query algorithm based on a knowledge graph, which considers the user's location semantic information and POIs to effectively discover the user's community preference. In order to achieve this goal, the algorithm first used Tr-tree spatial index to improve the query efficiency and then introduced the community satisfaction degree model based on the knowledge graph to comprehensively evaluate whether POIs can better meet the preference of the user community.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, the QI-SCSA algorithm [31] and the algorithm anytimeQE-approx algorithm [35] are slightly improved (the missing data is completed) and compared with the PCAR method in this paper in terms of data missing rate. After completing the data by PCAR algorithm, the PIPKQ algorithm in this paper can be compared with the BA algorithm [12], PQA algorithm [13] and DSAS algorithm [36] (extended DSAS algorithm, scored data points meeting different user requirements, and returned k Skyline result sets from high to low according to data point scores, and the extended algorithm was called EDS) in terms of k value, number of POIs, and the number of query objects. To ensure the validity of the experiment, we took an average of 30 queries for analysis.…”

Section: Experiments Analysismentioning

confidence: 99%

See 1 more Smart Citation

Multiuser Incomplete Preference K-Nearest Neighbor Query Method Based on Differential Privacy in Road Network

Zhang

Song

2023

IJGI

View full text Add to dashboard Cite

In view of the existing research in the field of k-nearest neighbor query in the road network, the incompleteness of the query user’s preference for data objects and the privacy protection of the query results are not considered, this paper proposes a multiuser incomplete preference k-nearest neighbor query algorithm based on differential privacy in the road network. The algorithm is divided into four parts; the first part proposes a multiuser incomplete preference completion algorithm based on association rules. The algorithm firstly uses the frequent pattern tree proposed in this paper to mine frequent item sets, then uses frequent item sets to mine strong correlation rules, and finally completes multiuser incomplete preference based on strong correlation rules. The second part proposes attribute preference weight coefficient based on multiuser‘ s different preferences and clusters users accordingly. The third part compares the dominance of the query object, filters the data with low dominance, and performs a k-neighbor query. The fourth part proposes a privacy budget allocation method based on differential privacy technology. The method uses the Laplace mechanism to add noise to the result release and balance the privacy and availability of data. Theoretical research and experimental analysis show that the proposed method can better deal with the multiuser incomplete preference k-nearest neighbor query and privacy protection problems in the road network.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Experiments Analysismentioning

confidence: 99%

Multiuser Incomplete Preference K-Nearest Neighbor Query Method Based on Differential Privacy in Road Network

Zhang

Song

2023

IJGI

View full text Add to dashboard Cite

show abstract

“…Point of interest (POI) [8], [20], [21], [22], [23], [24], [21], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [9], [37] E-commerce [38], [39], [40], [34], [41], [21], [22], [24], [42], [43], [3] Travel and tourism [24], [44], [45], [46], [47], [48], [6], [49] Entertainment [48], [50], [31], [24] Others [51], [18], [8], [52], [53], [6], [54], [49]…”

Section: Application Domain Papersmentioning

confidence: 99%

Location-Aware Recommender System: A review of Application Domains and Current Developmental Processes

Haruna¹,

Musa²,

Yunusa³

et al. 2022

SITech

View full text Add to dashboard Cite

Recommender systems (RS) have been widely used to extract relevant and meaningful information from a vast body of data, to make appropriate suggestions to users with different preferences in various domains of applications. However, despite the success of the early recommendation systems, they suffer from two major challenges of cold start and data sparsity. Traditional RS consider an interaction between user and item (2D), neglecting contextual information such as location, until fairly recently. The contexts extend traditional RS to multi-dimension interaction and provides a useful information that allow recommendations to be more personalized. Surprisingly, taking these contexts such as location, into consideration eliminates the challenges of traditional RS. Location-Aware Recommender System (LARS) takes user's location into account as an additional context. The combination allows the prediction of spatial items, items closest to the users, to reduce information overload and was proved to be more effective than earlier RS. In this research, we provide a systematic literature of the existing literature in LARS from 2010 to 2021, focusing on the state-of-the-art methodologies, the domain of applications, and trends of publications in LARS. The paper proposed several models of LARS based on the traditional RS methodologies, providing future directions to researchers. Despite numerous reviews available on LARS, a review that proposed several LARS techniques were not found in the literature. The results indicated that the trend of publication in LARS is growing exponentially and that the field is getting attention rapidly with the number of publications on the rise every year.

show abstract

“…Zhang et al [47] proposed the use of the historical information of each user in the group to automatically set the group preference and its weight in the social graph. Furthermore, several works suggested to focus future research on the development of new approaches for (i) assessing the relevance of the query results, for instance, by using realworld data collected from the Web [45]; and (ii) training knowledge graphs, for instance, by using deep learning technologies to intelligently perceive the user community preference information and choose the best POI to retrieve [61]. In addition, a look at new kinds of geosocial queries is also suggested by the surveyed works.…”

Section: Open Challenges Idmentioning

confidence: 99%

Query Processing of Geosocial Data in Location-Based Social Networks

D’Ulizia

Grifoni

Ferri

2021

IJGI

View full text Add to dashboard Cite

The increasing use of social media and the recent advances in geo-positioning technologies have produced a great amount of geosocial data, consisting of spatial, textual, and social information, to be managed and queried. In this paper, we focus on the issue of query processing by providing a systematic literature review of geosocial data representations, query processing methods, and evaluation approaches published over the last two decades (2000–2020). The result of our analysis shows the categories of geosocial queries proposed by the surveyed studies, the query primitives and the kind of access method used to retrieve the result of the queries, the common evaluation metrics and datasets used to evaluate the performance of the query processing methods, and the main open challenges that should be faced in the near future. Due to the ongoing interest in this research topic, the results of this survey are valuable to many researchers and practitioners by gaining an in-depth understanding of the geosocial querying process and its applications and possible future perspectives.

show abstract

Knowledge Graph-Based Spatial-Aware User Community Preference Query Algorithm for LBSNs

Cited by 12 publications

References 27 publications

Multiuser Incomplete Preference K-Nearest Neighbor Query Method Based on Differential Privacy in Road Network

Multiuser Incomplete Preference K-Nearest Neighbor Query Method Based on Differential Privacy in Road Network

Location-Aware Recommender System: A review of Application Domains and Current Developmental Processes

Query Processing of Geosocial Data in Location-Based Social Networks

Contact Info

Product

Resources

About