A Graph-Based Taxonomy of Recommendation Algorithms and Systems in LBSNs

Kefalas, Pavlos; Symeonidis, Panagiotis; Manolopoulos, Yannis

doi:10.1109/tkde.2015.2496344

Cited by 41 publications

(17 citation statements)

References 54 publications

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“…Corbellini et al [8] explored an architecture and platform for developing distributed recommendation algorithms, in which the recommendation time is reduced by modifying job distribution and non-invasive tuning strategies. In the medical field, valuable patient-oriented recommendations were introduced in [46,7,17]. Zheng et al introduced a context neighbor recommender platform, which integrates contexts via neighbors for recommendations [46].…”

Section: Related Workmentioning

confidence: 99%

“…Chen et al proposed an autocratic decision-making system using group recommendation methods [7]. In [17], Kefalas et al explored a graph-based taxonomy approach for recommendation algorithms and systems. A wearable assistant for gait training for Parkinson's disease was introduced in [24], which works with the freezing of gait in out-of-the-laboratory environments.…”

Section: Related Workmentioning

confidence: 99%

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A disease diagnosis and treatment recommendation system based on big data mining and cloud computing

Chen

Rong

et al. 2018

Information Sciences

151

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It is crucial to provide compatible treatment schemes for a disease according to various symptoms at different stages. However, most classification methods might be ineffective in accurately classifying a disease that holds the characteristics of multiple treatment stages, various symptoms, and multi-pathogenesis. Moreover, there are limited exchanges and cooperative actions in disease diagnoses and treatments between different departments and hospitals. Thus, when new diseases occur with atypical symptoms, inexperienced doctors might have difficulty in identifying them promptly and accurately. Therefore, to maximize the utilization of the advanced medical technology of developed hospitals and the rich medical knowledge of experienced doctors, a Disease Diagnosis and Treatment Recommendation System (DDTRS) is proposed in this paper. First, to effectively identify disease symptoms more accurately, a Density-Peaked Clustering Analysis (DPCA) algorithm is introduced for disease-symptom clustering. In addition, association analyses on Disease-Diagnosis (D-D) rules and Disease-Treatment (D-T) rules are conducted by the Apriori algorithm separately. The appropriate diagnosis and treatment schemes are recommended for patients and inexperienced doctors, even if they are in a limited therapeutic environment. Moreover, to reach the goals of high performance and low latency response, we implement a parallel solution for DDTRS using the Apache Spark cloud platform. Extensive experimental results demonstrate that the proposed DDTRS realizes disease-symptom clustering effectively and derives disease treatment recommendations intelligently and accurately.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A disease diagnosis and treatment recommendation system based on big data mining and cloud computing

Chen

Rong

et al. 2018

Information Sciences

151

View full text Add to dashboard Cite

show abstract

“…Spatial data mining gained huge attention in social media networking domain after social media networking sites started collecting user spatial data (Bao et al, 2015;Kefalas et al, 2016). Spatial co-location mining (Celik, 2015;Celik et al, 2008;Yu, 2016) and spatial clustering (Hu and Sung, 2005;Tung et al, 2001) are main topics of spatial data mining which could be used in social media datasets.…”

Section: Related Workmentioning

confidence: 99%

Fast Ss-Ilm: A Computationally Efficient Algorithm to Discover Socially Important Locations

Dokuz

Çelik

2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Socially important locations are places which are frequently visited by social media users in their social media lifetime. Discovering socially important locations provide several valuable information about user behaviours on social media networking sites. However, discovering socially important locations are challenging due to data volume and dimensions, spatial and temporal calculations, location sparseness in social media datasets, and inefficiency of current algorithms. In the literature, several studies are conducted to discover important locations, however, the proposed approaches do not work in computationally efficient manner. In this study, we propose Fast SS-ILM algorithm by modifying the algorithm of SS-ILM to mine socially important locations efficiently. Experimental results show that proposed Fast SS-ILM algorithm decreases execution time of socially important locations discovery process up to 20%.

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“…In recent years, many methods have been proposed to derive location and trip recommendations based on social media data (e.g., Flickr photos and Foursquare check-ins) [32,33]. For example, De Choudhury et al (2010) [34] extracted tourists' Flickr photos, aggregated them into a location graph and constructed travel itineraries by considering users' available time.…”

Section: Location Recommendation Using Geotagged Social Media Datamentioning

confidence: 99%

Context-Aware Location Recommendation Using Geotagged Photos in Social Media

Huang

2016

IJGI

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Abstract:Recently, the increasing availability of digital cameras and the rapid advances in social media have led to the accumulation of a large number of geotagged photos, which may reflect people's travel experiences in different cities and can be used to generate location recommendations for tourists. Research on this aspect mainly focused on providing personalized recommendations matching a tourist's travel preferences, while ignoring the context of the visit (e.g., weather, season and time of the day) that potentially influences his/her travel behavior. This article explores context-aware methods to provide location recommendations matching a tourist's travel preferences and visiting context. Specifically, we apply clustering methods to detect touristic locations and extract travel histories from geotagged photos on Flickr. We then propose a novel context similarity measure to quantify the similarity between any two contexts and develop three context-aware collaborative filtering methods, i.e., contextual pre-filtering, post-filtering and modeling. With these methods, location recommendations like "in similar contexts, other tourists similar to you often visited . . . " can be provided to the current user. Results of the evaluation with a publicly-available Flickr photo collection show that these methods are able to provide a tourist with location recommendations matching his/her travel preferences and visiting context. More importantly, compared to other state-of-the-art methods, the proposed methods, which employ the introduced context similarity measure, can provide tourists with significantly better recommendations. While Flickr data have been used in this study, these context-aware collaborative filtering (CaCF) methods can also be extended for other kinds of travel histories, such as GPS trajectories and Foursquare check-ins, to provide context-aware recommendations.

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A Graph-Based Taxonomy of Recommendation Algorithms and Systems in LBSNs

Cited by 41 publications

References 54 publications

A disease diagnosis and treatment recommendation system based on big data mining and cloud computing

A disease diagnosis and treatment recommendation system based on big data mining and cloud computing

Fast Ss-Ilm: A Computationally Efficient Algorithm to Discover Socially Important Locations

Context-Aware Location Recommendation Using Geotagged Photos in Social Media

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