25,517 research outputs found
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
Link Prediction in Complex Networks: A Survey
Link prediction in complex networks has attracted increasing attention from
both physical and computer science communities. The algorithms can be used to
extract missing information, identify spurious interactions, evaluate network
evolving mechanisms, and so on. This article summaries recent progress about
link prediction algorithms, emphasizing on the contributions from physical
perspectives and approaches, such as the random-walk-based methods and the
maximum likelihood methods. We also introduce three typical applications:
reconstruction of networks, evaluation of network evolving mechanism and
classification of partially labelled networks. Finally, we introduce some
applications and outline future challenges of link prediction algorithms.Comment: 44 pages, 5 figure
A Latent Parameter Node-Centric Model for Spatial Networks
Spatial networks, in which nodes and edges are embedded in space, play a
vital role in the study of complex systems. For example, many social networks
attach geo-location information to each user, allowing the study of not only
topological interactions between users, but spatial interactions as well. The
defining property of spatial networks is that edge distances are associated
with a cost, which may subtly influence the topology of the network. However,
the cost function over distance is rarely known, thus developing a model of
connections in spatial networks is a difficult task.
In this paper, we introduce a novel model for capturing the interaction
between spatial effects and network structure. Our approach represents a unique
combination of ideas from latent variable statistical models and spatial
network modeling. In contrast to previous work, we view the ability to form
long/short-distance connections to be dependent on the individual nodes
involved. For example, a node's specific surroundings (e.g. network structure
and node density) may make it more likely to form a long distance link than
other nodes with the same degree. To capture this information, we attach a
latent variable to each node which represents a node's spatial reach. These
variables are inferred from the network structure using a Markov Chain Monte
Carlo algorithm.
We experimentally evaluate our proposed model on 4 different types of
real-world spatial networks (e.g. transportation, biological, infrastructure,
and social). We apply our model to the task of link prediction and achieve up
to a 35% improvement over previous approaches in terms of the area under the
ROC curve. Additionally, we show that our model is particularly helpful for
predicting links between nodes with low degrees. In these cases, we see much
larger improvements over previous models
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