Context-Aware Friend Recommendation for Location Based Social Networks using Random Walk

Bagci, Hakan; Karagöz, Pınar

doi:10.1145/2872518.2890466

Cited by 52 publications

(34 citation statements)

References 17 publications

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“…[37,50,70,71,75,77,78,90,92,100,[103][104][105][106][107]109,111,128,130,133,136,137,160,161,164,165] f-measure To measure the harmonic mean of recall and precision…”

Section: Precisionmentioning

confidence: 99%

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Context-Aware Recommender System: A Review of Recent Developmental Process and Future Research Direction

et al. 2017

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Intelligent data handling techniques are beneficial for users; to store, process, analyze and access the vast amount of information produced by electronic and automated devices. The leading approach is to use recommender systems (RS) to extract relevant information from the vast amount of knowledge. However, early recommender systems emerged without the cognizance to contextualize information regarding users' recommendations. Considering the historical methodological limitations, Context-Aware Recommender Systems (CARS) are now deployed, which leverage contextual information in addition to the classical two-dimensional search processes, providing better-personalized user recommendations. This paper presents a review of recent developmental processes as a fountainhead for the research of a context-aware recommender system. This work contributes by taking an integrated approach to the complete CARS developmental process, unlike other review papers, which only address a specific aspect of the CARS process. First, an in-depth review is presented pertaining to the state-of-the-art and classified literature, considering the domain of the application models, filters, extraction and evaluation approaches. Second, viewpoints are presented relating to the extraction of literature with analysis on the merit and demerit of each, and the evolving processes between them. Finally, the outstanding challenges and opportunities for future research directions are highlighted.

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“…[37,50,70,71,75,77,78,90,92,100,[103][104][105][106][107]109,111,128,130,133,136,137,160,161,164,165] f-measure To measure the harmonic mean of recall and precision…”

Section: Precisionmentioning

confidence: 99%

“…[35,37,71,75,78,90,103,109,161,165] Mean Absolute Error (MAE) To measure the accuracy of rating predictions…”

Section: Precisionmentioning

confidence: 99%

Context-Aware Recommender System: A Review of Recent Developmental Process and Future Research Direction

et al. 2017

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“…In [25,40,41], they focus on the effect of geographical-influence on POI recommendation and propose different methods to utilize the geographical neighborhood characteristics. In [17,1], more weight is given to the influence of potential check-ins from friends. Moreover, there has been many studies combing geographical-influence and socialnetwork-influence, such as [14,42].…”

Section: Related Workmentioning

confidence: 99%

“…-Encryption E: Let m be a message in Z N . It is encrypted by selecting a random number r in Z * N and computing c = E(m) = g m r N mod N 2 (1) where N and g are from the public key pk and c is the ciphertext of m. -Decryption D: The ciphertext c is decrypted by computing…”

Section: Preliminarymentioning

confidence: 99%

Protecting multi-party privacy in location-aware social point-of-interest recommendation

Wang¹,

Liu²,

Li³

et al. 2018

World Wide Web

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Q, et al. (2018) Protecting multi-party privacy in location-aware social point-ofinterest recommendation. World Wide Web. Available: http:// dx.Abstract Point-of-interest (POI) recommendation has attracted much interest recently because of its significant business potential. Data used in POI recommendation (e.g., user-location check-in matrix) are much more sparse than that used in traditional item (e.g., book and movie) recommendation, which leads to more serious cold start problem. Social POI recommendation has proved to be an effective solution, but most existing works assume that recommenders have access to all required data. This is very rare in practice because these data are generally owned by different entities who are not willing to share their data with others due to privacy and legal concerns. In this paper, we first propose PLAS, a protocol which enables effective POI recommendation without disclosing the sensitive data of every party getting involved in the recommendation. We formally show PLAS is secure in the semi-honest adversary model. To improve its performance. We then adopt the technique of cloaking area by which expensive distance computation over encrypted data is replaced by cheap operation over plaintext. In addition, we utilize the sparsity of check-ins to selectively publish data, thus reducing encryption cost and avoiding unnecessary computation over ciphertext. Experiments on two real datasets show that our protocol is feasible and can scale to large POI recommendation problems in practice.

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“…LBSNs also referred to as Geographic Social Networks or Geo-social Networks (SCEL- LATO et al, 2010;, have attracted big attention of scientific community, and consequently have enable many novel applications in different research issues, such as social network analysis PENTLAND;LAZER, 2008;MONTJOYE;BETTENCOURT, 2009;XIAO et al, 2010;CRANSHAW et al, 2010;QUERCIA et al, 2010;YING et al, 2010;YING et al, 2011;MA et al, 2012;BRAGA et al, 2012;XIAO et al, 2014;GRABOWICZ et al, 2014;XUE, 2014;BAO et al, 2015;BAGCI;KARAGOZ, 2016;XU-RUI;WEI-LI, 2016;LIAO et al, 2016), spatiotemporal data mining (ZHENG et al, 2009;MENG, 2011;HAI et al, 2012;HAI et al, 2013;JENSEN, 2013;HOANG;LIM, 2015;ASGHARI et al, 2015;, spatio-temporal databases DOYTSHER;GALON;KANZA, 2010;CHEN et al, 2010;TANG et al, 2011;HASHEM et al, 2013; (FERRARI et al, 2011;TAHRAT;TESSEIRE, 2012;LOGLISCI et al, 2012a;LOGLISCI et al, 2012b;BOUILLOT;PRIEDHORSKY;…”

Section: Location-based Social Networkmentioning

confidence: 99%

Mining user behavior in location-based social networks

Rebaza¹

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Context-Aware Friend Recommendation for Location Based Social Networks using Random Walk

Cited by 52 publications

References 17 publications

Context-Aware Recommender System: A Review of Recent Developmental Process and Future Research Direction

Context-Aware Recommender System: A Review of Recent Developmental Process and Future Research Direction

Protecting multi-party privacy in location-aware social point-of-interest recommendation

Mining user behavior in location-based social networks

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