180 research outputs found
Collaborating with Users in Proximity for Decentralized Mobile Recommender Systems
Typically, recommender systems from any domain, be it movies, music,
restaurants, etc., are organized in a centralized fashion. The service provider
holds all the data, biases in the recommender algorithms are not transparent to
the user, and the service providers often create lock-in effects making it
inconvenient for the user to switch providers. In this paper, we argue that the
user's smartphone already holds a lot of the data that feeds into typical
recommender systems for movies, music, or POIs. With the ubiquity of the
smartphone and other users in proximity in public places or public
transportation, data can be exchanged directly between users in a
device-to-device manner. This way, each smartphone can build its own database
and calculate its own recommendations. One of the benefits of such a system is
that it is not restricted to recommendations for just one user - ad-hoc group
recommendations are also possible. While the infrastructure for such a platform
already exists - the smartphones already in the palms of the users - there are
challenges both with respect to the mobile recommender system platform as well
as to its recommender algorithms. In this paper, we present a mobile
architecture for the described system - consisting of data collection, data
exchange, and recommender system - and highlight its challenges and
opportunities.Comment: Accepted for publication at the 2019 IEEE 16th International
Conference on Ubiquitous Intelligence and Computing (IEEE UIC 2019
Towards MANET-based Recommender Systems for Open Facilities
Nowadays, most recommender systems are based on a centralized architecture, which can cause crucial issues in terms of trust, privacy, dependability, and costs. In this paper, we propose a decentralized and distributed MANET-based (Mobile Ad-hoc NETwork) recommender system for open facilities. The system is based on mobile devices that collect sensor data about users locations to derive implicit ratings that are used for collaborative filtering recommendations. The mechanisms of deriving ratings and propagating them in a MANET network are discussed in detail. Finally, extensive experiments demonstrate the suitability of the approach in terms of different performance metrics. © 2021, The Author(s)
Self-Healing Protocols for Connectivity Maintenance in Unstructured Overlays
In this paper, we discuss on the use of self-organizing protocols to improve
the reliability of dynamic Peer-to-Peer (P2P) overlay networks. Two similar
approaches are studied, which are based on local knowledge of the nodes' 2nd
neighborhood. The first scheme is a simple protocol requiring interactions
among nodes and their direct neighbors. The second scheme adds a check on the
Edge Clustering Coefficient (ECC), a local measure that allows determining
edges connecting different clusters in the network. The performed simulation
assessment evaluates these protocols over uniform networks, clustered networks
and scale-free networks. Different failure modes are considered. Results
demonstrate the effectiveness of the proposal.Comment: The paper has been accepted to the journal Peer-to-Peer Networking
and Applications. The final publication is available at Springer via
http://dx.doi.org/10.1007/s12083-015-0384-
Trust models for mobile content-sharing applications
Using recent technologies such as Bluetooth, mobile users can share digital content (e.g., photos, videos)
with other users in proximity. However, to reduce the cognitive load on mobile users, it is important that
only appropriate content is stored and presented to them.
This dissertation examines the feasibility of having mobile users filter out irrelevant content by running
trust models. A trust model is a piece of software that keeps track of which devices are trusted (for
sending quality content) and which are not. Unfortunately, existing trust models are not fit for purpose.
Specifically, they lack the ability to: (1) reason about ratings other than binary ratings in a formal way;
(2) rely on the trustworthiness of stored third-party recommendations; (3) aggregate recommendations
to make accurate predictions of whom to trust; and (4) reason across categories without resorting to
ontologies that are shared by all users in the system.
We overcome these shortcomings by designing and evaluating algorithms and protocols with which
portable devices are able automatically to maintain information about the reputability of sources of
content and to learn from each other’s recommendations. More specifically, our contributions are:
1. An algorithm that formally reasons on generic (not necessarily binary) ratings using Bayes’ theorem.
2. A set of security protocols with which devices store ratings in (local) tamper-evident tables and
are able to check the integrity of those tables through a gossiping protocol.
3. An algorithm that arranges recommendations in a “Web of Trust” and that makes predictions of
trustworthiness that are more accurate than existing approaches by using graph-based learning.
4. An algorithm that learns the similarity between any two categories by extracting similarities between
the two categories’ ratings rather than by requiring a universal ontology. It does so automatically
by using Singular Value Decomposition.
We combine these algorithms and protocols and, using real-world mobility and social network data,
we evaluate the effectiveness of our proposal in allowing mobile users to select reputable sources of
content. We further examine the feasibility of implementing our proposal on current mobile phones by
examining the storage and computational overhead it entails. We conclude that our proposal is both
feasible to implement and performs better across a range of parameters than a number of current alternatives
From Social Data Mining to Forecasting Socio-Economic Crisis
Socio-economic data mining has a great potential in terms of gaining a better
understanding of problems that our economy and society are facing, such as
financial instability, shortages of resources, or conflicts. Without
large-scale data mining, progress in these areas seems hard or impossible.
Therefore, a suitable, distributed data mining infrastructure and research
centers should be built in Europe. It also appears appropriate to build a
network of Crisis Observatories. They can be imagined as laboratories devoted
to the gathering and processing of enormous volumes of data on both natural
systems such as the Earth and its ecosystem, as well as on human
techno-socio-economic systems, so as to gain early warnings of impending
events. Reality mining provides the chance to adapt more quickly and more
accurately to changing situations. Further opportunities arise by individually
customized services, which however should be provided in a privacy-respecting
way. This requires the development of novel ICT (such as a self- organizing
Web), but most likely new legal regulations and suitable institutions as well.
As long as such regulations are lacking on a world-wide scale, it is in the
public interest that scientists explore what can be done with the huge data
available. Big data do have the potential to change or even threaten democratic
societies. The same applies to sudden and large-scale failures of ICT systems.
Therefore, dealing with data must be done with a large degree of responsibility
and care. Self-interests of individuals, companies or institutions have limits,
where the public interest is affected, and public interest is not a sufficient
justification to violate human rights of individuals. Privacy is a high good,
as confidentiality is, and damaging it would have serious side effects for
society.Comment: 65 pages, 1 figure, Visioneer White Paper, see
http://www.visioneer.ethz.c
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