SgWalk: Location Recommendation by User Subgraph-Based Graph Embedding

Download
2021-01-01
Canturk, Deniz
Karagöz, Pınar
Popularity of Location-based Social Networks (LBSNs) provides an opportunity to collect massive multi-modal datasets that contain geographical information, as well as time and social interactions. Such data is a useful resource for generating personalized location recommendations. Such heterogeneous data can be further extended with notions of trust between users, the popularity of locations, and the expertise of users. Recently the use of Heterogeneous Information Network (HIN) models and graph neural architectures have proven successful for recommendation problems. One limitation of such a solution is capturing the contextual relationships between the nodes in the heterogeneous network. In location recommendation, spatial context is a frequently used consideration such that users prefer to get recommendations within their spatial vicinity. To solve this challenging problem, we propose a novel Heterogeneous Information Network (HIN) embedding technique, SgWalk, which explores the proximity between users and locations and generates location recommendations via subgraph-based node embedding. SgWalk follows four steps: building users subgraphs according to location context, generating random walk sequences over user subgraphs, learning embeddings of nodes in LBSN graph, and generating location recommendations using vector representation of the nodes. SgWalk is differentiated from existing techniques relying on meta-path or bi-partite graphs by means of utilizing the contextual user subgraph. In this way, it is aimed to capture contextual relationships among heterogeneous nodes more effectively. The recommendation accuracy of SgWalk is analyzed through extensive experiments conducted on benchmark datasets in terms of top-n location recommendations. The accuracy evaluation results indicate minimum 23% (@5 recommendation) average improvement in accuracy compared to baseline techniques and the state-of-the-art heterogeneous graph embedding techniques in the literature.

Suggestions

Time Preference aware Dynamic Recommendation Enhanced with Location, Social Network and Temporal Information
Ozsoy, Makbule Gulcin; Polat, Faruk; Alhajj, Reda (2016-08-21)
Social networks and location based social networks have many active users who provide various kind of data, such as where they have been, who their friends are, which items they like more, when they go to a venue. Location, social network and temporal information provided by them can be used by recommendation systems to give more accurate suggestions. Also, recommendation systems can provide dynamic recommendations based on the users' preferences, such that they can give different recommendations for differ...
Geo-social recommendations based on incremental tensor reduction and local path traversal
Symeniodis, Panagiotis; Papadimitriou, Alexis; Manolopoulos, Yannis; Karagöz, Pınar; Toroslu, İsmail Hakkı (2011-11-01)
Social networks have evolved with the combination of geographical data, into Geo-social networks (GSNs). GSNs give users the opportunity, not only to communicate with each other, but also to share images, videos, locations, and activities. The latest developments in GSNs incorporate the usage of location tracking services, such as GPS to allow users to “check in” at various locations and record their experience. In particular, users submit ratings or personal comments for their location/activity. The vast a...
SWARM-based data delivery in Social Internet of Things
Hasan, Mohammed Zaki; Al-Turjman, Fadi (Elsevier BV, 2019-03-01)
Social Internet of Things (SIoTs) refers to the rapidly growing network of connected objects and people that are able to collect and exchange data using embedded sensors. To guarantee the connectivity among these objects and people, fault tolerance routing has to be significantly considered. In this paper, we propose a bio-inspired particle multi-swarm optimization (PMSO) routing algorithm to construct, recover and select k-disjoint paths that tolerates the failure while satisfying quality of service (QoS) ...
Collective classification of user emotions in twitter
İleri, İbrahim; Karagöz, Pınar; Department of Computer Engineering (2015)
The recent explosion of social networks has generated a big amount of data including user opinions about varied subjects. For classifying the sentiment of user postings, many text-based techniques have been proposed in the literature. As a continuation of sentiment analysis, there are also studies on the emotion analysis. Because of the fact that many different emotions are needed to be dealt with at this point, the problem becomes much more complicated. In this thesis, a different user-centric approach is ...
Triadic co-clustering of users, issues and sentiments in political tweets
Koc, Sefa Sahin; Ozer, Mert; Toroslu, İsmail Hakkı; Davulcu, Hasan; Jordan, Jeremy (2018-06-15)
Social network data contains many hidden relationships. The most well known is the communities formed by users. Moreover, typical social network data, such as Twitter, can also be interpreted in terms of three-dimensional relationships; namely the users, issues discussed by the users, and terminology chosen by the users in these discussions. In this paper, we propose a new problem to generate co-clusters in these three dimensions simultaneously. There are three major differences between our problem and the ...
Citation Formats
D. Canturk and P. Karagöz, “SgWalk: Location Recommendation by User Subgraph-Based Graph Embedding,” IEEE ACCESS, vol. 9, pp. 134858–134873, 2021, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/93784.