FreeRec: an Anonymous and Distributed Personalization Architecture

We present and evaluate FreeRec, an anonymous decentral- ized peer-to-peer architecture, designed to bring personalization while protecting the privacy of its users. FreeRec's decentralized approach makes it independent of any entity wishing to collect personal data about users. At the same time, its onion-routing-like gossip-based overlay protocols effectively hide the association between users and their inter- est profiles without affecting the quality of personalization. The core of FreeRec consists of three layers of overlay protocols: the bottom layer, rps, consists of a standard random peer sampling protocol ensur- ing connectivity; the middle layer, PRPS, introduces anonymity by hid- ing users behind anonymous proxy chains, providing mutual anonymity; finally, the top clustering layer identifies for each anonymous user, a set of anonymous nearest neighbors. We demonstrate the effectiveness of FreeRec by building a decentralized and anonymous content dis- semination system. Our evaluation by simulation and through extensive PlanetLab experiments show that FreeRec effectively decouples users from their profiles without hampering the quality of personalized content delivery

A Multiagent Approach to Personalization and Assistance to Multiple Persons in a Smart Home

http://www.aaai.org/ocs/index.php/WS/AAAIW14/paper/download/8809/8371&sa=X&scisig=AAGBfm2W2ejiuEPthMsyGE4AgBRTA_1HfAInternational audienceLocalization, personalization, activity recognition, and cognitive assistance are key issues in research on smart homes for cognitively impaired people. Most of the current solutions rely on the presence of solely one person in the residence. To actively consider the interaction of the smart home inhabitant with their caregivers, nurses, doctors and people sharing their home, this paper proposes a multi-agent approach to transparently locate, identify, and ease the collaboration between distributed personalization and assistance services. Based on Bayesian filtering localization using anonymous sensors, the multi-person localization process provides information on each occupant presence, either incoming or outgoing. This information is then used for personalization and assistance

NoTube – making TV a medium for personalized interaction

In this paper, we introduce NoTube’s vision on deploying semantics in interactive TV context in order to contextualize distributed applications and lift them to a new level of service that provides context-dependent and personalized selection of TV content. Additionally, lifting content consumption from a single-user activity to a community-based experience in a connected multi-device environment is central to the project. Main research questions relate to (1) data integration and enrichment - how to achieve unified and simple access to dynamic, growing and distributed multimedia content of diverse formats? (2) user and context modeling - what is an appropriate framework for context modeling, incorporating task-, domain and device-specific viewpoints? (3) context-aware discovery of resources - how could rather fuzzy matchmaking between potentially infinite contexts and available media resources be achieved? (4) collaborative architecture for TV content personalization - how can the combined information about data, context and user be put at disposal of both content providers and end-users in the view of creating extremely personalized services under controlled privacy and security policies? Thus, with the grand challenge in mind - to put the TV viewer back in the driver's seat – we focus on TV content as a medium for personalized interaction between people based on a service architecture that caters for a variety of content metadata, delivery channels and rendering devices

Generic system architecture for context-aware, distributed recommendation

In the existing literature on recommender systems, it is difficult to find an architecture for large-scale implementation. Often, the architectures proposed in papers are specific to an algorithm implementation or a domain. Thus, there is no clear architectural starting point for a new recommender system. This paper presents an architecture blueprint for a context-aware recommender system that provides scalability, availability, and security for its users. The architecture also contributes the dynamic ability to switch between single-device (offline), client-server (online), and fully distributed implementations. From this blueprint, a new recommender system could be built with minimal design and implementation effort regardless of the application.Electrical and Computer Engineerin

An Agent Enabled System for Personalizing Wireless Mobile Services

Handheld wireless devices such as cellular phones and personal digital assistants (PDAs) have limited memory, storage, and processing power. In addition, small screens and limited input facilities make entering information tedious. It is therefore important that wireless mobile applications optimize resource usage and minimize input effort imposed on the user. One way is to download to the client only the information most relevant to the user, then present that information effectively, taking into account the user\u27s preferences and history as well as the task at hand. This personalization approach minimizes the information to be displayed. In this paper, we present a mobile agent-based system for personalizing mobile services; we use mobile agents simply because such autonomous software entities have characteristics that can benefit mobile devices and the wireless environment. We introduce the tiered architecture of the proposed system and the functions of the different components; then we discuss how we use the Composite/Capabilities Preferences/Profile (CC/PP) in personalizing wireless mobile services. A proof of concept implementation has been developed using Java technologies

User model interoperability in education: sharing learner data using the experience API and distributed ledger technology

Learning analytics and data mining require gathering and exchanging learner data for further processing and designing of activities tailored to learner’s characteristics, context, and needs. Currently, systems that store learners’ attributes should, ideally, be operated and controlled by responsible and trustworthy authorities that guarantee the protection and sovereignty of data and use objective criteria to protect and represent all parties’ interests. This chapter introduces a peer-to-peer method for storing and exchanging learner data with minimal trust. The proposed approach, underpinned by the Experience API standard, eliminates the need of a mediator authority by using distributed ledger technology

On the Design of Collective Applications

Paper presented at SocialCom 2009 Vancouver, 2009In this paper we define collective applications as those that employ the aggregated distinct behaviours of individuals in a crowd to shape their environment and to provide structure and influence in that environment. Such behaviour can be seen in most systems that aggregate user-generated content, whether or not that is the intention of the designers or contributors. We identify the necessary features of such applications and observe that they pose a particularly wicked set of design problems, because important characteristics of the system, including processing and presentation, reside outside the program in the behaviour of the crowd itself. We suggest some approaches to dealing with these problems