Makram Bouzid

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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(More)
This paper describes an architecture model for multiagent systems that was developed in the European project LEAP (Lightweight Extensible Agent Platform). Its main feature is a set of generic services that are implemented independently of the agents and can be installed into the agents by the application developer in a flexible way. Moreover, two(More)
We propose the design of a privacy-preservingpersonalization middleware that enables the enduser to avail of personalized services without disclosing sensitive profile information to the content/service-provider or any third party for that matter. Our solution relies on a distributed infrastructure comprising local clients running on end-user devices and a(More)
—We present the MobiLife approach to contextual adaptation of multimodal mobile applications for individual users. In particular, we show that integration of user-interface adaptation, contextualization and personalization components of the MobiLife system architecture enables a personal and situational context-dependent provisioning of applications such as(More)
The Locality Sensitive Hashing (LSH) technique of scalably finding nearest-neighbors can be adapted to enable discovering similar users while preserving their privacy. The key idea is to compute the user profile on the end-user device, apply LSH on the local profile, and use the LSH cluster identifier as the interest group identifier of a user. By(More)