Recommendations based on a heterogeneous spatio-temporal social network

Kefalas, Pavlos; Symeonidis, Panagiotis; Manolopoulos, Yannis

doi:10.1007/s11280-017-0454-0

Cited by 35 publications

(24 citation statements)

References 19 publications

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“…Moreover, in Kefalas et al. ( 2018 ), a recommendation system was proposed that incorporates user time-varying preferences as well. In particular, the recommendation was based on a tripartite graph, consisting of users, locations and sessions .…”

Section: Related Workmentioning

confidence: 99%

Point-of-interest lists and their potential in recommendation systems

Stamatelatos

Drosatos

Gyftopoulos

et al. 2021

Inf Technol Tourism

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Location based social networks, such as Foursquare and Yelp, have inspired the development of novel recommendation systems due to the massive volume and multiple types of data that their users generate on a daily basis. More recently, research studies have been focusing on utilizing structural data from these networks that relate the various entities, typically users and locations. In this work, we investigate the information contained in unique structural data of social networks, namely the lists or collections of items, and assess their potential in recommendation systems. Our hypothesis is that the information encoded in the lists can be utilized to estimate the similarities amongst POIs and, hence, these similarities can drive a personalized recommendation system or enhance the performance of an existing one. This is based on the fact that POI lists are user generated content and can be considered as collections of related POIs. Our method attempts to extract these relations and express the notion of similarity using graph theoretic, set theoretic and statistical measures. Our approach is applied on a Foursquare dataset of two popular destinations in northern Greece and is evaluated both via an offline experiment and against the opinions of local populace that we obtain via a user study. The results confirm the existence of rich similarity information within the lists and the effectiveness of our approach as a recommendation system.

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Section: Related Workmentioning

confidence: 99%

Point-of-interest lists and their potential in recommendation systems

Stamatelatos

Drosatos

Gyftopoulos

et al. 2021

Inf Technol Tourism

View full text Add to dashboard Cite

show abstract

“…Previous models were failed to adequately capture user time-varying preferences, so Kefalas et al [192] focused on the time dimension for the friend recommendation system. They constructed a hybrid tripartite graph with the heterogeneous spatio-temporal method consisting of sessions, users, and locations to capture the similarity between users and user location.…”

Section: ) Location-based Friend Recommendationsmentioning

confidence: 99%

Linked Open Data in Location-Based Recommendation System on Tourism Domain: A Survey

Yochum

Chang

et al. 2020

IEEE Access

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Linked open data is a relatively new topic area with great potential in a wide range of fields. In the tourism domain, many studies are using linked open data to address the problem of location-based recommendation by integrating data with other linked open datasets to enrich data and tourism content for reacting to the needs of tourists. This work aims not only to present a systematic review and mapping of the linked open data in location-based recommendation system on tourism domain, but also to provide an overview of the current research status in the area. First, we classify journal papers in this area from 2001 to 2018 by the year of publication. Second, we analyze and categorize journal papers by the different recommendation applications including problem formulations, data collections, proposed algorithms/systems, and experimental results. Third, we group the linked open data sources used in location-based recommendation system on tourism. Next, we summarize the research achievements and present the distribution of the different categories of location-based recommendation applications via linked open data. Last, we also guide the possible future research direction for the linked open data in location-based recommendations on tourism.

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“…Ma et al [29] proposed two meta-path based proximities to learn vertex representations in the heterogeneous graphs. Some studies [30]- [32] considered the time information of interactions on a bipartite graph and proposed dynamic graph embedding methods, but they focused more on selecting all the edges connected to a vertex in chronological order, and the sequences they obtained only contained one type of vertex, so the dynamic interaction process was ignored.…”

Section: B Graph Embeddingmentioning

confidence: 99%

DBGE: Employee Turnover Prediction Based on Dynamic Bipartite Graph Embedding

et al. 2020

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The issue of employee turnover is always critical for companies, and accurate predictions can help them prepare in time. Most past studies on employee turnover have focused on analyzing impact factors or using simple network centrality measures. In this paper, we study the problem from a completely new perspective by modeling users' historical job records as a dynamic bipartite graph. Specifically, we propose a bipartite graph embedding method with temporal information called dynamic bipartite graph embedding (DBGE) to learn the vector representation of employees and companies. Our approach not only considers the relations between employees and companies but also incorporates temporal information embedded in consecutive work records. We first define the Horary Random Walk on a bipartite graph to generate a sequence for each vertex in chronological order. Then, we employ the skip-gram model to obtain a temporal low-dimensional vector representation for each vertex and apply machine learning methods to predict employee turnover behavior by combining embedded features with employees' basic information. Experiments on a real-world dataset collected from one of China's largest online professional social networks show that the features learned through DBGE can significantly improve turnover prediction performance. Moreover, experiments on public Amazon and Taobao datasets show that our approach achieves better performance in the link prediction and visualization task than other graph embedding methods that do not consider temporal information. INDEX TERMS Graph embedding, employee turnover prediction, dynamic bipartite graph, horary random walk.

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Recommendations based on a heterogeneous spatio-temporal social network

Cited by 35 publications

References 19 publications

Point-of-interest lists and their potential in recommendation systems

Point-of-interest lists and their potential in recommendation systems

Linked Open Data in Location-Based Recommendation System on Tourism Domain: A Survey

DBGE: Employee Turnover Prediction Based on Dynamic Bipartite Graph Embedding

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