3,579 research outputs found
Empirical analysis of web-based user-object bipartite networks
Understanding the structure and evolution of web-based user-object networks
is a significant task since they play a crucial role in e-commerce nowadays.
This Letter reports the empirical analysis on two large-scale web sites,
audioscrobbler.com and del.icio.us, where users are connected with music groups
and bookmarks, respectively. The degree distributions and degree-degree
correlations for both users and objects are reported. We propose a new index,
named collaborative clustering coefficient, to quantify the clustering behavior
based on the collaborative selection. Accordingly, the clustering properties
and clustering-degree correlations are investigated. We report some novel
phenomena well characterizing the selection mechanism of web users and outline
the relevance of these phenomena to the information recommendation problem.Comment: 6 pages, 7 figures and 1 tabl
Behavior patterns of online users and the effect on information filtering
Understanding the structure and evolution of web-based user-object bipartite
networks is an important task since they play a fundamental role in online
information filtering. In this paper, we focus on investigating the patterns of
online users' behavior and the effect on recommendation process. Empirical
analysis on the e-commercial systems show that users have significant taste
diversity and their interests for niche items highly overlap. Additionally,
recommendation process are investigated on both the real networks and the
reshuffled networks in which real users' behavior patterns can be gradually
destroyed. Our results shows that the performance of personalized
recommendation methods is strongly related to the real network structure.
Detail study on each item shows that recommendation accuracy for hot items is
almost maximum and quite robust to the reshuffling process. However, niche
items cannot be accurately recommended after removing users' behavior patterns.
Our work also is meaningful in practical sense since it reveals an effective
direction to improve the accuracy and the robustness of the existing
recommender systems.Comment: 8 pages, 6 figure
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
Information Filtering on Coupled Social Networks
In this paper, based on the coupled social networks (CSN), we propose a
hybrid algorithm to nonlinearly integrate both social and behavior information
of online users. Filtering algorithm based on the coupled social networks,
which considers the effects of both social influence and personalized
preference. Experimental results on two real datasets, \emph{Epinions} and
\emph{Friendfeed}, show that hybrid pattern can not only provide more accurate
recommendations, but also can enlarge the recommendation coverage while
adopting global metric. Further empirical analyses demonstrate that the mutual
reinforcement and rich-club phenomenon can also be found in coupled social
networks where the identical individuals occupy the core position of the online
system. This work may shed some light on the in-depth understanding structure
and function of coupled social networks
Temporal effects in trend prediction: identifying the most popular nodes in the future
Prediction is an important problem in different science domains. In this
paper, we focus on trend prediction in complex networks, i.e. to identify the
most popular nodes in the future. Due to the preferential attachment mechanism
in real systems, nodes' recent degree and cumulative degree have been
successfully applied to design trend prediction methods. Here we took into
account more detailed information about the network evolution and proposed a
temporal-based predictor (TBP). The TBP predicts the future trend by the node
strength in the weighted network with the link weight equal to its exponential
aging. Three data sets with time information are used to test the performance
of the new method. We find that TBP have high general accuracy in predicting
the future most popular nodes. More importantly, it can identify many potential
objects with low popularity in the past but high popularity in the future. The
effect of the decay speed in the exponential aging on the results is discussed
in detail
Tag-Aware Recommender Systems: A State-of-the-art Survey
In the past decade, Social Tagging Systems have attracted increasing
attention from both physical and computer science communities. Besides the
underlying structure and dynamics of tagging systems, many efforts have been
addressed to unify tagging information to reveal user behaviors and
preferences, extract the latent semantic relations among items, make
recommendations, and so on. Specifically, this article summarizes recent
progress about tag-aware recommender systems, emphasizing on the contributions
from three mainstream perspectives and approaches: network-based methods,
tensor-based methods, and the topic-based methods. Finally, we outline some
other tag-related works and future challenges of tag-aware recommendation
algorithms.Comment: 19 pages, 3 figure
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