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Identifying Users From Their Rating Patterns
[article]
2012
arXiv
pre-print
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The test dataset comprises 5,450 ratings for which the user label is missing, but the household label is provided. The challenge required to identify the user labels for the ratings in the test set. ...
Using a model that leverages on this fact, we are able to identify users within a known household with an accuracy of approximately 96 ...
Section 3, makes a crucial use of temporal patterns in the users rating behavior. ...
arXiv:1207.6379v1
fatcat:dkpqujh72jhinhjefrsrdh7lnm
Identifying users from their rating patterns
2011
Proceedings of the 2nd Challenge on Context-Aware Movie Recommendation - CAMRa '11
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The test dataset comprises 5 450 ratings for which the user label is missing, but the household label is provided. The challenge required to identify the user labels for the ratings in the test set. ...
Using a model that leverages on this fact, we are able to identify users within a known household with an accuracy of approximately 96 % (i.e. misclassification rate around 4 %). ...
Section 3, makes a crucial use of temporal patterns in the users rating behavior. ...
doi:10.1145/2096112.2096120
fatcat:vl7jcctbwfb5zdw3preocvz34y
Identifying the perceptive users for online social systems
2017
PLoS ONE
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Moreover, we investigate the behavior patterns of the perceptive users from three dimensions: User activity, correlation characteristics of user rating series and user reputation. ...
By tracking the ratings given to the rewarded objects, we present a method to identify the user perceptibility, which is defined as the capability that a user can identify these objects at their early ...
Afterwards, the high perceptibility users will be identified based on the user collective behavior patterns analysed from the new ratings in the rating systems (Process P4) by the generalization of random ...
doi:10.1371/journal.pone.0178118
pmid:28704382
pmcid:PMC5509131
fatcat:dlpidtibtfg3hjhnlqzjoixryi
Longitudinal Analysis of Heart Rate and Physical Activity Collected from Smartwatches
[article]
2022
arXiv
pre-print
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In our work, we gathered longitudinal data points, e.g., heart rate and physical activity, from various brands of SWs worn by 1,014 users. ...
Our analysis shows three common heart rate patterns during sleep but two common patterns during the day. ...
Our study aims to identify users' new behavioral patterns and behavioral dynamics of physical activity along with heart rate changes. ...
arXiv:2211.08628v1
fatcat:khea7tm5nrfjba5trgumttzyqa
Identifying Smartphone Users based on their Activity Patterns via Mobile Sensing
2017
Procedia Computer Science
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Due to ease of use and access, many users are using smartphones to store their private data, such as personal identifiers and bank account details. ...
Due to ease of use and access, many users are using smartphones to store their private data, such as personal identifiers and bank account details. ...
Vector Machine (SVM) for identifying different users based on their activity patterns. ...
doi:10.1016/j.procs.2017.08.349
fatcat:v3uoviunlbdltlteaoyp7gqjhu
MapRat
2012
Proceedings of the VLDB Endowment
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MapRat allows a user to systematically explore, visualize and understand user rating patterns of input item(s) so as to make an informed decision quickly. ...
Collaborative rating sites such as IMDB and Yelp have become rich resources that users consult to form judgments about and choose from among competing items. ...
SM is most useful in identifying reviewer preferences. Additionally, a user can choose the reviewer group she most identifies with and choose their aggregate rating. ...
doi:10.14778/2367502.2367554
fatcat:in3r77a2anblxkppfwnuaelugy
Research Aligned Analysis on Web Access Behavioral Pattern Mining for User Identificationa
2019
International Journal of Engineering and Advanced Technology
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Mining user activity from web logs can helps in finding hidden information about the user access pattern which reveals the web access behaviour of the users. ...
Clustering and Classification techniques are used for web user identification. Clustering is the task of grouping similar patterns for web user identification. ...
the user behaviour patterns from the weblog and so on. ...
doi:10.35940/ijeat.f9552.088619
fatcat:wxgynqsh5zbmxisl4jrqcvhgku
Enhancing recommender systems under volatile userinterest drifts
2009
Proceeding of the 18th ACM conference on Information and knowledge management - CIKM '09
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The type of a given user's interest pattern is identified through the density of the corresponding rating graph and the continuity of the corresponding rating chain. ...
To this end, we first define four types of interest patterns to understand users' rating behaviors and analyze the properties of these patterns. ...
Then we identify different user interest patterns from all users' rating series. If a user's rating series is identified as a CNP, it will be removed. ...
doi:10.1145/1645953.1646112
dblp:conf/cikm/CaoCYX09
fatcat:cuhfgqhlu5ac5p5sm3toqh7iue
Using Recommendations to Help Novices to Reuse Design Knowledge
[chapter]
2011
Lecture Notes in Computer Science
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The use of pattern languages is not so straightforward since its users have to identify the patterns they need, browsing the language and understanding both the benefits and trade-offs of each pattern ...
Novice designers might benefit from tools that assist them in this learning task. ...
-Novice users might find the recommendation tool useful not only to improve their designs but to learn about patterns and their relation. ...
doi:10.1007/978-3-642-21530-8_35
fatcat:kph6ngqss5b43hj73ivnb3e3c4
Your Privilege Gives Your Privacy Away: An Analysis of a Home Security Camera Service
2020
IEEE INFOCOM 2020 - IEEE Conference on Computer Communications
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Furthermore, we identify three privacy risks and explore them in detail. We find that paid users are more likely to be exposed to attacks due to their heavier usage patterns. ...
Our study takes two perspectives: (i) we explore the per-user behaviour, identifying core clusters of users; and (ii) we build on this analysis to extract and predict privacy-compromising insight. ...
Rate change risk Finally, we explore the potential to identify activity changes on a camera feed via bit rate monitoring, e.g. identifying a person shifting from sitting to walking. ...
doi:10.1109/infocom41043.2020.9155516
dblp:conf/infocom/Li0TX20
fatcat:55kq62riprbfpedalpyteulihq
User Verification Leveraging Gait Recognition for Smartphone Enabled Mobile Healthcare Systems
2015
IEEE Transactions on Mobile Computing
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patterns from run-time accelerometer measurements to perform robust user verification under various walking speeds. ...
In this paper, we propose a user verification system leveraging unique gait patterns derived from acceleration readings to detect possible user spoofing in mobile healthcare systems. ...
The presence of user spoofing causes the newly identified gait patterns to be dramatically different from a user's normal gait patterns and hence such attacks can be detected. ...
doi:10.1109/tmc.2014.2365185
fatcat:knhbtrzugrhwzjimhw7yrdfelm
Sovereignty of the Apps: There's more to Relevance than Downloads
[article]
2016
arXiv
pre-print
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We study their impact on a large-scale database of app-usage data from a community of 339,842 users and more than 213,667 apps. ...
We identify typical trends in app popularity and classify applications into archetypes. From these, we can distinguish, for instance, trendsetters from copycat apps. ...
Unfortunately identifying these users from the Carat data is not possible. ...
arXiv:1611.10161v1
fatcat:uj6b6ezvzff53cclv6ocmvslcu
RecencyMiner: mining recency-based personalized behavior from contextual smartphone data
2019
Journal of Big Data
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The volatility of usage behavior will also vary from user-to-user. ...
Behavioral patterns of smartphone users may vary greatly between individuals in different contexts-for example, temporal, spatial, or social contexts. ...
Acknowledgements The authors would like to thank the administrative staff of Swinburne University of Technology, Melbourne, Australia, for their support while doing this work and experiment in their post-graduate ...
doi:10.1186/s40537-019-0211-6
fatcat:dqbhlaesjbbdxaub5k5vwm7c6m
Continuous authentication of smartphone users based on activity pattern recognition using passive mobile sensing
2018
Journal of Network and Computer Applications
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To address these 19 challenges, a novel continuous authentication scheme is presented in this study, which recognizes smartphone 20 users on the basis of their physical activity patterns using accelerometer ...
Nowadays, loads of people 13 tend to store different types of private and sensitive data in their smartphones including bank account details, 14 personal identifiers, accounts credentials, and credit card ...
users based on their activity patterns as shown in 91 Fig. 1 1. ...
doi:10.1016/j.jnca.2018.02.020
fatcat:4tu22aitbzaqxjbmpnsbkakhde
An Effective Hybrid Recommender Using Metadata-based Conceptualization and Temporal Semantics
2016
International Journal of Recent Contributions from Engineering, Science & IT
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This framework also includes an online process that identifies the conceptual drift of the usage dynamically. ...
In this paper a hybrid recommender framework is developed that considers Meta data based conceptual semantics and the temporal patterns on top of the history of the usage. ...
The output of the algorithm gives k neighbors and their matched access patterns. From this pattern the ratings are aggregated and the recommendations will be populated.
H. ...
doi:10.3991/ijes.v4i3.5943
fatcat:jaq5o3ir7neb3pizy5yxl3nube
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