2,859 research outputs found
How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility
Recommendation systems are ubiquitous and impact many domains; they have the
potential to influence product consumption, individuals' perceptions of the
world, and life-altering decisions. These systems are often evaluated or
trained with data from users already exposed to algorithmic recommendations;
this creates a pernicious feedback loop. Using simulations, we demonstrate how
using data confounded in this way homogenizes user behavior without increasing
utility
Recommender Systems
The ongoing rapid expansion of the Internet greatly increases the necessity
of effective recommender systems for filtering the abundant information.
Extensive research for recommender systems is conducted by a broad range of
communities including social and computer scientists, physicists, and
interdisciplinary researchers. Despite substantial theoretical and practical
achievements, unification and comparison of different approaches are lacking,
which impedes further advances. In this article, we review recent developments
in recommender systems and discuss the major challenges. We compare and
evaluate available algorithms and examine their roles in the future
developments. In addition to algorithms, physical aspects are described to
illustrate macroscopic behavior of recommender systems. Potential impacts and
future directions are discussed. We emphasize that recommendation has a great
scientific depth and combines diverse research fields which makes it of
interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports
The development, status and trends of recommender systems: a comprehensive and critical literature review
Recommender systems have been used in many fields of research and business applications. In this paper, a comprehensive and critical review of the literature on recommender systems is provided. A classification mechanism of recommender systems is proposed. The review pays attention to and covers the recommender system algorithms, application areas and data mining techniques published in relevant peer-reviewed journals between 2001 and 2013. The development of the field, status and trends are analyzed and discussed in the paper
Effective Mechanism for Social Recommendation of News
Recommendation systems represent an important tool for news distribution on
the Internet. In this work we modify a recently proposed social recommendation
model in order to deal with no explicit ratings of users on news. The model
consists of a network of users which continually adapts in order to achieve an
efficient news traffic. To optimize network's topology we propose different
stochastic algorithms that are scalable with respect to the network's size.
Agent-based simulations reveal the features and the performance of these
algorithms. To overcome the resultant drawbacks of each method we introduce two
improved algorithms and show that they can optimize network's topology almost
as fast and effectively as other not-scalable methods that make use of much
more information
Parsimonious and Adaptive Contextual Information Acquisition in Recommender Systems
Also published online by CEUR Workshop Proceedings (CEUR-WS.org, ISSN 1613-0073)Â Context-Aware Recommender System (CARS) models are
trained on datasets of context-dependent user preferences
(ratings and context information). Since the number of
context-dependent preferences increases exponentially with
the number of contextual factors, and certain contextual in-
formation is still hard to acquire automatically (e.g., the
user's mood or for whom the user is buying the searched
item) it is fundamental to identify and acquire those factors
that truly in
uence the user preferences and the ratings. In
particular, this ensures that (i) the user e ort in specifying
contextual information is kept to a minimum, and (ii) the
system's performance is not negatively impacted by irrele-
vant contextual information. In this paper, we propose a
novel method which, unlike existing ones, directly estimates
the impact of context on rating predictions and adaptively
identi es the contextual factors that are deemed to be useful
to be elicited from the users. Our experimental evaluation
shows that it compares favourably to various state-of-the-art
context selection methods
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