Towards Psychometrics-Based Friend Recommendations in Social Networking Services

Beierle, Felix; Grunert, Kai; Göndör, Sebastian; Schlüter, Viktor

doi:10.1109/aims.2017.22

Cited by 17 publications

(16 citation statements)

References 36 publications

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“…E-Smalltalker aims at incentivizing real-life smalltalk by exchanging interests profiles between users in proximity [15]. In our own previous work, we proposed using all available smartphone data and automatically comparing it in order to determine similarity in terms of interest and personality [1], [16]. In [17], Yang and Hwang proposed a mobile recommender system for point-of-interest (POI) recommendations that utilizes data exchanged in a device-to-device manner.…”

Section: Related Workmentioning

confidence: 99%

“…Some companies offer frameworks for device-to-device communication. Ueoaa AG's p2pkit 16 is a multi-platform framework for seamless device-to-device computing. However, the code does not seem actively maintained 17 .…”

Section: Frameworkmentioning

confidence: 99%

“…15 http://thaliproject.org/Android-and-iOS-interop/, accessed 2020-07-20 16 http://p2pkit.io/, accessed 2020-07-20 17 https://github.com/Uepaa-AG, accessed 2020-07-20 18 https://www.opengarden.com/firechat/, accessed 2020-07-20 19 https://en.wikipedia.org/wiki/FireChat, accessed 2020-07-20 20 https://github.com/opengarden, accessed 2020-07-20 21 https://www.bridgefy.me/, accessed 2020-07-20 22 https://www.bridgefy.me/pricing.html, accessed 2020-07-20 23 https://github.com/bridgefy/bridgefy-ios-developer/blob/master/ README.md, accessed 2020-07-20 24 https://www.nintendo.com/3ds/built-in-software/streetpass/how-itworks and http://us.playstation.com/psvita/apps/psvita-app-near.html, both accessed 2020-04-10…”

Section: Frameworkmentioning

confidence: 99%

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MobRec — Mobile Platform for Decentralized Recommender Systems

Beierle

Egger

2020

IEEE Access

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Recommender systems recommend new movies, music, restaurants, etc. Typically, service providers organize such systems in a centralized way, holding all the data. Biases in the recommender systems are not transparent to the user and lock-in effects might make it inconvenient for the user to switch providers. In this paper, we present the concept, design, and implementation of MobRec, a mobile platform that decentralizes the data collection, data storage, and recommendation process. MobRec's architecture does not need any backend and solely consists of the users' smartphones, which already contain the users' preferences and ratings. Being in proximity in public places or public transportation, data is exchanged in a device-to-device manner, building local databases that can recommend new items. One of biggest challenges of such a system is the implementation of unobtrusive device-to-device data exchange on off-the-shelf Android devices and iPhones. MobRec facilitates such data exchange, building on Google Nearby Messages with Bluetooth Low Energy. We achieve the successful exchange of data within 3 to 4 minutes, making it suitable for the described scenario. We demonstrate the feasibility of decentralized recommender systems and provide blueprints for the development of seamless multi-platform device-to-device communication.

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Section: Related Workmentioning

confidence: 99%

Section: Frameworkmentioning

confidence: 99%

Section: Frameworkmentioning

confidence: 99%

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MobRec — Mobile Platform for Decentralized Recommender Systems

Beierle

Egger

2020

IEEE Access

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“…Figure 3, and propose the following. 4 Definition 1 (CMS-cosSim). Let R and S be two CMSs with the same dimensions d × w and utilizing the same hash functions.…”

Section: A Cosine Similarities For Cbfs and Cmssmentioning

confidence: 99%

Do You Like What I Like? Similarity Estimation in Proximity-Based Mobile Social Networks

Beierle¹

2018

2018 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications/ 12th IEEE International

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While existing social networking services tend to connect people who know each other, people show a desire to also connect to yet unknown people in physical proximity. Existing research shows that people tend to connect to similar people. Utilizing technology in order to stimulate human interaction between strangers, we consider the scenario of two strangers meeting. On the example of similarity in musical taste, we develop a solution for the problem of similarity estimation in proximitybased mobile social networks. We show that a single exchange of a probabilistic data structure between two devices can closely estimate the similarity of two users -without the need to contact a third-party server. We introduce metrics for fast and spaceefficient approximation of the Dice coefficient of two multisets -based on the comparison of two Counting Bloom Filters or two Count-Min Sketches. Our analysis shows that utilizing a single hash function minimizes the error when comparing these probabilistic data structures. The size that should be chosen for the data structure depends on the expected average number of unique input elements. Using real user data, we show that a Counting Bloom Filter with a single hash function and a length of 128 is sufficient to accurately estimate the similarity between two multisets representing the musical tastes of two users. Our approach is generalizable for any other similarity estimation of frequencies represented as multisets.

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“…Beirle et al [34] point to the widespread belief that Cambridge Analytica and their version of the model that originated with Youyou, Kosinski and Stillwell, played a significant, if not essential part in the election of Donald Trumpsomething that Cambridge Analytica have also claimed themselves.…”

Section: When Psychometrics Entered Political Social Media Campaigningmentioning

confidence: 99%

The Ethics of Psychometrics in Social Media: A Rawlsian Approach

Bay

2018

Proceedings of the 51st Hawaii International Conference on System Sciences

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Targeted social media advertising based on psychometric user profiling has emerged as an effective way of reaching individuals who are predisposed to accept and be persuaded by the advertising message. In the political realm, the use of psychometrics appears to have been used to spread both information and misinformation through social media in recent elections in the U.S. and Europe, partially resulting in the current, public debate about 'fake news'. This paper questions the ethics of these methods, both in a commercial context and in the context of democratic processes. The ethical approach is based on the theoretical, contractarian work of John Rawls which serves as a lens through which the author examines whether the rights of citizens, as Rawls attributes them, are violated by this practice. The paper concludes that within a Rawlsian framework, use of psychometrics in commercial advertising on social media platforms is not necessarily unethical, since the user enters freely into a contract that allows for psychometrics to be used, and because this type of advertising is not necessary for full participation in society. The opposite is the case for political information, and thus, the paper concludes that use of psychometrics in political campaigning violates several of Rawls' ethical maxims.

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Towards Psychometrics-Based Friend Recommendations in Social Networking Services

Cited by 17 publications

References 36 publications

MobRec — Mobile Platform for Decentralized Recommender Systems

MobRec — Mobile Platform for Decentralized Recommender Systems

Do You Like What I Like? Similarity Estimation in Proximity-Based Mobile Social Networks

The Ethics of Psychometrics in Social Media: A Rawlsian Approach

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