Preference, context and communities

Xu, Ye; Lin, Mu; Lu, Hong; Cardone, Giuseppe; Lane, Nicholas D.; Chen, Zhenyu; Campbell, Andrew T.; Choudhury, Tanzeem

doi:10.1145/2493988.2494333

Cited by 99 publications

(7 citation statements)

References 21 publications

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“…Zhang et al [43] built a Bayesian network relying on time, date, location, and previously used applications in inference. Several nearest neighbors-based methods were shown to provide great flexibility for the task [24,40,6]. Baeza-Yates et al [5] tested forecasting methods, including Tree Augmented Naive Bayes and a decision tree based on the C4.5 algorithm.…”

Section: Related Workmentioning

confidence: 99%

Federated Privacy-preserving Collaborative Filtering for On-Device Next App Prediction

Sayapin¹,

Balitskiy²,

Bershatsky³

et al. 2023

Preprint

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In this study, we propose a novel SeqMF model to solve the problem of predicting the next app launch during mobile device usage. Although this problem can be represented as a classical collaborative filtering problem, it requires proper modification since the data are sequential, the user feedback is distributed among devices and the transmission of users' data to aggregate common patterns must be protected against leakage. According to such requirements, we modify the structure of the classical matrix factorization model and update the training procedure to sequential learning. Since the data about user experience are distributed among devices, the federated learning setup is used to train the proposed sequential matrix factorization model. One more ingredient of the proposed approach is a new privacy mechanism that guarantees the protection of the sent data from the users to the remote server. To demonstrate the efficiency of the proposed model we use publicly available mobile user behavior data. We compare our model with sequential rules and models based on the frequency of app launches. The comparison is conducted in static and dynamic environments. The static environment evaluates how our model processes sequential data compared to competitors. Therefore, the standard train-validation-test evaluation procedure is used. The dynamic environment emulates the real-world scenario, where users generate new data by running apps on devices, and evaluates our model in this case. Our experiments show that the proposed model provides comparable quality with other methods in the static environment. However, more importantly, our method achieves a better privacy-utility trade-off than competitors in the dynamic environment, which provides more accurate simulations of real-world usage.

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Section: Related Workmentioning

confidence: 99%

Federated Privacy-preserving Collaborative Filtering for On-Device Next App Prediction

Sayapin¹,

Balitskiy²,

Bershatsky³

et al. 2023

Preprint

View full text Add to dashboard Cite

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“…Erazo and Pino proposed a model for predicting execution time of midair gestures [71]. Other works predicted the applications that will be used next [72,73]. In the context of public displays, Huber et al analysed passerby's feet position to detect the user's intention and accordingly adapt the rendered content [74].…”

Section: Modelling and Predicting Public Display Users' Behaviourmentioning

confidence: 99%

Predicting mid-air gestural interaction with public displays based on audience behaviour

Gentile

Khamis

Milazzo

et al. 2020

International Journal of Human-Computer Studies

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Knowledge about the expected interaction duration and expected distance from which users will interact with public displays can be useful in many ways. For example, knowing upfront that a certain setup will lead to shorter interactions can nudge space owners to alter the setup. If a system can predict that incoming users will interact at a long distance for a short amount of time, it can accordingly show shorter versions of content (e.g., videos/advertisements) and employ at-a-distance interaction modalities (e.g., mid-air gestures). In this work, we propose a method to build models for predicting users' interaction duration and distance in public display environments, focusing on mid-air gestural interactive displays. First, we report our findings from a field study showing that multiple variables, such as audience size and behaviour, significantly influence interaction duration and distance. We then train predictor models using contextual data, based on the same variables. By applying our method to a mid-air gestural interactive public display deployment, we build a model that predicts interaction duration with an average error of about 8 s, and interaction distance with an average error of about 35 cm. We discuss how researchers and practitioners can use our work to build their own predictor models, and how they can use them to optimise their deployment.

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“…Before presenting the system design, we discuss our observation on context and data usage patterns. Prior works [7–9] provide the insights of the comparison of the usage time among different applications, that is, how long the user uses the application. The data usage, however, is different from the application usage time.…”

Section: Context Versus Data Usagementioning

confidence: 99%

Detecting Phantom Data Usage on Smartphones with Analysis of Contextual Information

Jiang

Zhou

2015

International Journal of Distributed Sensor Networks

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With the wide development of smartphones, mobile data usage has enjoyed rapid growth in recent years. Unfortunately many users are plagued with Phantom Data Usage (PDU), which refers to the unexpected mobile data usage that does not accord with user perception. We investigate about 400 real PDU issues and find the causes of PDU are not only the exceptions of applications, for example, software bugs or malware, but also the user's personalized misuse. Based on the observations that each user preserves specific data usage patterns under particular environmental context, we present PDS, a PDU detection system, which automatically detects whether the current data usage is consumed as expected. Results from our evaluation experiments show that 72% of PDU cases detected by PDS are confirmed by users.

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Preference, context and communities

Cited by 99 publications

References 21 publications

Federated Privacy-preserving Collaborative Filtering for On-Device Next App Prediction

Federated Privacy-preserving Collaborative Filtering for On-Device Next App Prediction

Predicting mid-air gestural interaction with public displays based on audience behaviour

Detecting Phantom Data Usage on Smartphones with Analysis of Contextual Information

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