Armen Aghasaryan
Bell Labs
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Publication
Featured researches published by Armen Aghasaryan.
international conference on user modeling adaptation and personalization | 2010
Christophe Senot; Dimitre Kostadinov; Makram Bouzid; Jérôme Picault; Armen Aghasaryan; Cédric Bernier
Today most of existing personalization systems (e.g content recommenders, or targeted ad) focus on individual users and ignore the social situation in which the services are consumed However, many human activities are social and involve several individuals whose tastes and expectations must be taken into account by the service providers When a group profile is not available, different profile aggregation strategies can be applied to recommend adequate content and services to a group of users based on their individual profiles In this paper, we consider an approach intended to determine the factors that influence the choice of an aggregation strategy We present a preliminary evaluation made on a real large-scale dataset of TV viewings, showing how group interests can be predicted by combining individual user profiles through an appropriate strategy The conducted experiments compare the group profiles obtained by aggregating individual user profiles according to various strategies to the “reference” group profile obtained by directly analyzing group consumptions.
international joint conference on artificial intelligence | 2011
Christophe Senot; Dimitre Kostadinov; Makram Bouzid; Jérôme Picault; Armen Aghasaryan
Most of the existing personalization systems such as content recommenders or targeted ads focus on individual users and ignore the social situation in which the services are consumed. However, many human activities are social and involve several individuals whose tastes and expectations must be taken into account by the system. When a group profile is not available, different profile aggregation strategies can be applied to recommend adequate items to a group of users based on their individual profiles. We consider an approach intended to determine the factors that influence the choice of an aggregation strategy. We present evaluations made on a large-scale dataset of TV viewings, where real group interests are compared to the predictions obtained by combining individual user profiles according to different strategies.
information integration and web-based applications & services | 2008
Sofiane Abbar; Mokrane Bouzeghoub; Dimitre Kostadinov; Stéphane Lopes; Armen Aghasaryan; Stéphane Betgé-Brezetz
Access to relevant information, adapted to users needs, preferences and environment, is a challenge in many applications running in content delivery platforms, like IPTV, VoD and mobile Video. In order to provide users with personalized content, applications use various techniques such as content recommendation, content filtering, preference-driven queries, etc. These techniques exploit different knowledge organized into profiles and contexts. However, there is not a common understanding of these concepts and there is no clear foundation of what a personalized access model should be. This paper contributes to this concern by providing, through a meta model, a clear distinction between profile and context, and by providing a set of services which constitutes a basement to the definition of a personalized access model (PAM). Our PAM definition allows applications to interoperate in multiple personalization scenarios, including, preference-based recommendation, context-aware content delivery, personalized access to multiple contents, etc. Concepts and services proposed are tightly defined with respect to real applications requirements provided by Alcatel-Lucent.
conference on privacy, security and trust | 2011
Mahmoud Ghorbel; Armen Aghasaryan; Stéphane Betgé-Brezetz; Marie-Pascale Dupont; Guy-Bertrand Kamga; Sophie Piekarec
In this paper, we present a privacy control mechanism called PDE (Privacy Data Envelope) allowing users to protect their privacy sensitive content travelling over social and communication networks. Our solution is based on privacy policies expressed by the user and associated with his content. This approach makes use of a decentralized architecture carried out through a PDE feature that has to be added to the existing application access tools like email clients and web browsers. A prototype has been developed to embody the PDE paradigm and to illustrate a scenario where such envelopes cross the boundaries of enterprise social networks and other communications tools. Preliminary performance evaluations were done helping the understanding of the PDE plug-in behaviors and computation overhead.
international workshop on security | 2011
David Pergament; Armen Aghasaryan; Jean-Gabriel Ganascia; Stéphane Betgé-Brezetz
The Friends-Oriented Reputation Privacy Score (FORPS) system provides a smart and simple way to help end-users managing their privacy in a social network. It aims to prevent a non-desirable propagation of personal sensitive data. FORPS built privacy sensitivity profile by understanding what are the category of themes, the category of objects and the behavioral factors that are important to social network users. FORPS takes full advantage of the knowledge available in a social network from the perspective of a given user, in particular extracted from the data accessible via his friends. More precisely, our approach consists in making a deep analysis of the behavior of somebody who would like to establish connection with the given user in order to estimate the risk of potential violation of his privacy.
privacy security risk and trust | 2011
Yang Wang; Armen Aghasaryan; Arvind Shrihari; David Pergament; Guy Bertrand Kamga; Stéphane Betgé-Brezetz
With the boom of social media, it has become increasingly easier for ordinary people to not only post their own content but share other peoples content on the Internet. In this paper, we conceptualize a growing problem of moving user data - once a user posts some content on the Internet, the data is largely out of her control, the content can be forwarded to or shared with other people, applications or websites, potentially causing various privacy issues. We present a technical solution that aims to provide users flexible fine-grained control over their moving data. Our system builds upon the ideas of data envelope with sticky policy, reactive access control, and privacy scores. Users can specify and enforce sticky policies of their data through our data envelope plug-ins. Our reactive access control mechanism allows users to grant access to their data on the fly, extending the pre-defined sticky policies to better fit with the dynamic nature of peoples sharing practices. Finally, the privacy score helps users make decisions about data requests by providing relevant privacy risk assessment information about the requesters.
Bell Labs Technical Journal | 2011
Emmanuel Marilly; Christophe Senot; Xavier Andrieu; Bertrand Boidart; Armen Aghasaryan; Alexis Germaneau
The WellCom platform enables the creation, delivery, and management of advanced personalized and interactive multimedia applications and services in a distributed home environment. End users obtain easy and seamless access to interactive and personalized television (TV) services as well as TV-related applications through their mobile terminals. The supporting devices and technologies include TV sets, mobile terminals for interaction with TV content and to deliver personalized services directly to the user; and near field communication (NFC)/Bluetooth technologies which combine with an “easy-pairing” mechanism for access to interactive programs and social applications. Individual interactivity opens up new possibilities for group experience of TV content. For example, users can themselves take part in a broadcast TV quiz or compete with friends. Personalization allows service providers to tap into new revenue streams such as targeted advertisements on mobile phones.
2017 20th Conference on Innovations in Clouds, Internet and Networks (ICIN) | 2017
Armen Aghasaryan; Makram Bouzid; Dimitre Kostadinov
In this paper, we present an approach for automated profiling of cloud-based distributed applications. The failure dependencies within or between application nodes can be methodically elucidated by dynamically applying a series of unitary perturbations on the underlying computing resources. Each such perturbation in a node acts as a stimulus which propagates to performance meters of dependent nodes and reveals correlations and causal relations between the respective entities. We have developed an instrumented framework for methodical elucidation of these dependencies which covers an extensive set of failure situations. The prime application of our approach is the behavior learning of a distributed application under various resource insufficiency conditions for Quality of Service management and Root Cause Analysis.
international conference on intelligence in next generation networks | 2009
Armen Aghasaryan; Stéphane Betgé-Brezetz; Muralidharan S. Kodialam; Sarit Mukherjee; Christophe Senot; Yann Toms; Limin Wang
Mastering knowledge of user profile is one of the technical cornerstones for service providers who handle a large amount of service consumption data and are well positioned to dynamically infer user interests. This paper presents a technology allowing to gather usage data from different multimedia services, create and track users profiles in real-time and monetize them by targeting content or other personalized services. In particular, we address the critical issue of a large service provider having to manage services in both controlled domains (where the metadata structure is under its control) and domains out of its control. We present a solution based on keyword inference and mapping to enable profiling and personalization on such heterogeneous domains.
UM | 2010
Christophe Senot; Dimitre Kostadinov; Makram Bouzid; Jérôme Picault; Armen Aghasaryan; Cédric Bernier