Time-Aware User Identification with Topic Models

Lesaege, Clément; Schnitzler, François; Lambert, Anne; Vigouroux, Jean-Ronan

doi:10.1109/icdm.2016.0126

Cited by 7 publications

(4 citation statements)

References 10 publications

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“…Temporal information is also an important factor for distinguishing individual users in a household (Campos et al, ; Iguchi et al, ; Lesaege et al, ). For example, day of the week and hour of the day were demonstrated to be effective time features for identifying active household members (Campos et al, ).…”

Section: Related Work and Discussionmentioning

confidence: 99%

“…In this article, our primary goal is not to distinguish users in the same household through time. As known (Lesaege et al, ), different users in the same household are reluctant to login with different accounts when they want to watch TV. Thus, in real applications, we do not know how many users are living in a household, let alone who is responsible for each video viewing record.…”

Section: Related Work and Discussionmentioning

confidence: 99%

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Temporal patterns of the online video viewing behavior of smart TV viewers

Lian

Chen

Lin

et al. 2018

Asso for Info Science & Tech

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In recent years, millions of households have shifted from traditional TVs to smart TVs for viewing online videos on TV screens. In this article, we perform extensive analyses on a large-scale online video viewing log on smart TVs. Because time influences almost every aspect of our lives, our aim is to understand temporal patterns of the online video viewing behavior of smart TV viewers at the crowd level. First, we measure the amount of time per hour spent in watching online videos on smart TV by each household on each day. By applying clustering techniques, we identify eight daily patterns whose peak hours occur in different segments of the day. The differences among households can be characterized by three types of temporal habits. We also uncover five periodic weekly patterns. There seems to be a circadian rhythm at the crow level. Further analysis confirms that there exists a holiday effect in the online video viewing behavior on smart TVs. Finally, we investigate the popularity variations of different video categories over the day. The obtained insights shed light on how we can partition a day to improve the performance of time-aware video recommendations for smart TV viewers.

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Section: Related Work and Discussionmentioning

confidence: 99%

Section: Related Work and Discussionmentioning

confidence: 99%

Temporal patterns of the online video viewing behavior of smart TV viewers

Lian

Chen

Lin

et al. 2018

Asso for Info Science & Tech

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“…Many works [8], [29], [44] aimed at discovery theft's behavioral pattern. Bursztein et al [40] pointed out that identity thieves usually behave in two possible suspicious patterns, i.e., (1) behaving unlike the majority of users; (2) behaving only unlike the victim.…”

Section: Suspicious Behavior Simulationmentioning

confidence: 99%

“…Ruan et al [30] conducted a study on online user behavior by collecting and analyzing user clickstreams of a well known OSN. Lesaege et al [29] developed a topic model extending the Latent Dirichlet Allocation (LDA) to identify the active users. Viswanath et al [44] presented a technique based on Principal Component Analysis (PCA) that accurately modeled the "like" behavior of normal users in Facebook and identified significant deviations from it as anomalous behaviors.…”

Section: Literature Reviewmentioning

confidence: 99%

Fusing Behavioral Projection Models for Identity Theft Detection in Online Social Networks

Wang

Yang

Cui

et al. 2019

IEEE Trans. Comput. Soc. Syst.

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In this work, we aim at building a bridge from poor behavioral data to an effective, quick-response, and robust behavior model for online identity theft detection. We concentrate on this issue in online social networks (OSNs) where users usually have composite behavioral records, consisting of multi-dimensional low-quality data, e.g., offline check-ins and online user generated content (UGC). As an insightful result, we find that there is a complementary effect among different dimensions of records for modeling users' behavioral patterns. To deeply exploit such a complementary effect, we propose a joint model to capture both online and offline features of a user's composite behavior. We evaluate the proposed joint model by comparing with some typical models on two real-world datasets: Foursquare and Yelp. In the widely-used setting of theft simulation (simulating thefts via behavioral replacement), the experimental results show that our model outperforms the existing ones, with the AUC values 0.956 in Foursquare and 0.947 in Yelp, respectively. Particularly, the recall (True Positive Rate) can reach up to 65.3% in Foursquare and 72.2% in Yelp with the corresponding disturbance rate (False Positive Rate) below 1%. It is worth mentioning that these performances can be achieved by examining only one composite behavior (visiting a place and posting a tip online simultaneously) per authentication, which guarantees the low response latency of our method. This study would give the cybersecurity community new insights into whether and how a real-time online identity authentication can be improved via modeling users' composite behavioral patterns.

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