2,166 research outputs found
Beyond Personalization: Research Directions in Multistakeholder Recommendation
Recommender systems are personalized information access applications; they
are ubiquitous in today's online environment, and effective at finding items
that meet user needs and tastes. As the reach of recommender systems has
extended, it has become apparent that the single-minded focus on the user
common to academic research has obscured other important aspects of
recommendation outcomes. Properties such as fairness, balance, profitability,
and reciprocity are not captured by typical metrics for recommender system
evaluation. The concept of multistakeholder recommendation has emerged as a
unifying framework for describing and understanding recommendation settings
where the end user is not the sole focus. This article describes the origins of
multistakeholder recommendation, and the landscape of system designs. It
provides illustrative examples of current research, as well as outlining open
questions and research directions for the field.Comment: 64 page
Distributed Bayesian Probabilistic Matrix Factorization
Matrix factorization is a common machine learning technique for recommender
systems. Despite its high prediction accuracy, the Bayesian Probabilistic
Matrix Factorization algorithm (BPMF) has not been widely used on large scale
data because of its high computational cost. In this paper we propose a
distributed high-performance parallel implementation of BPMF on shared memory
and distributed architectures. We show by using efficient load balancing using
work stealing on a single node, and by using asynchronous communication in the
distributed version we beat state of the art implementations
Filter Bubbles in Recommender Systems: Fact or Fallacy -- A Systematic Review
A filter bubble refers to the phenomenon where Internet customization
effectively isolates individuals from diverse opinions or materials, resulting
in their exposure to only a select set of content. This can lead to the
reinforcement of existing attitudes, beliefs, or conditions. In this study, our
primary focus is to investigate the impact of filter bubbles in recommender
systems. This pioneering research aims to uncover the reasons behind this
problem, explore potential solutions, and propose an integrated tool to help
users avoid filter bubbles in recommender systems. To achieve this objective,
we conduct a systematic literature review on the topic of filter bubbles in
recommender systems. The reviewed articles are carefully analyzed and
classified, providing valuable insights that inform the development of an
integrated approach. Notably, our review reveals evidence of filter bubbles in
recommendation systems, highlighting several biases that contribute to their
existence. Moreover, we propose mechanisms to mitigate the impact of filter
bubbles and demonstrate that incorporating diversity into recommendations can
potentially help alleviate this issue. The findings of this timely review will
serve as a benchmark for researchers working in interdisciplinary fields such
as privacy, artificial intelligence ethics, and recommendation systems.
Furthermore, it will open new avenues for future research in related domains,
prompting further exploration and advancement in this critical area.Comment: 21 pages, 10 figures and 5 table
Discovering the Impact of Knowledge in Recommender Systems: A Comparative Study
Recommender systems engage user profiles and appropriate filtering techniques
to assist users in finding more relevant information over the large volume of
information. User profiles play an important role in the success of
recommendation process since they model and represent the actual user needs.
However, a comprehensive literature review of recommender systems has
demonstrated no concrete study on the role and impact of knowledge in user
profiling and filtering approache. In this paper, we review the most prominent
recommender systems in the literature and examine the impression of knowledge
extracted from different sources. We then come up with this finding that
semantic information from the user context has substantial impact on the
performance of knowledge based recommender systems. Finally, some new clues for
improvement the knowledge-based profiles have been proposed.Comment: 14 pages, 3 tables; International Journal of Computer Science &
Engineering Survey (IJCSES) Vol.2, No.3, August 201
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
On Sampling Strategies for Neural Network-based Collaborative Filtering
Recent advances in neural networks have inspired people to design hybrid
recommendation algorithms that can incorporate both (1) user-item interaction
information and (2) content information including image, audio, and text.
Despite their promising results, neural network-based recommendation algorithms
pose extensive computational costs, making it challenging to scale and improve
upon. In this paper, we propose a general neural network-based recommendation
framework, which subsumes several existing state-of-the-art recommendation
algorithms, and address the efficiency issue by investigating sampling
strategies in the stochastic gradient descent training for the framework. We
tackle this issue by first establishing a connection between the loss functions
and the user-item interaction bipartite graph, where the loss function terms
are defined on links while major computation burdens are located at nodes. We
call this type of loss functions "graph-based" loss functions, for which varied
mini-batch sampling strategies can have different computational costs. Based on
the insight, three novel sampling strategies are proposed, which can
significantly improve the training efficiency of the proposed framework (up to
times speedup in our experiments), as well as improving the
recommendation performance. Theoretical analysis is also provided for both the
computational cost and the convergence. We believe the study of sampling
strategies have further implications on general graph-based loss functions, and
would also enable more research under the neural network-based recommendation
framework.Comment: This is a longer version (with supplementary attached) of the KDD'17
pape
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