663 research outputs found
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
Finding community structure in very large networks
The discovery and analysis of community structure in networks is a topic of
considerable recent interest within the physics community, but most methods
proposed so far are unsuitable for very large networks because of their
computational cost. Here we present a hierarchical agglomeration algorithm for
detecting community structure which is faster than many competing algorithms:
its running time on a network with n vertices and m edges is O(m d log n) where
d is the depth of the dendrogram describing the community structure. Many
real-world networks are sparse and hierarchical, with m ~ n and d ~ log n, in
which case our algorithm runs in essentially linear time, O(n log^2 n). As an
example of the application of this algorithm we use it to analyze a network of
items for sale on the web-site of a large online retailer, items in the network
being linked if they are frequently purchased by the same buyer. The network
has more than 400,000 vertices and 2 million edges. We show that our algorithm
can extract meaningful communities from this network, revealing large-scale
patterns present in the purchasing habits of customers
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
Finding and Recommending Scholarly Articles
The rate at which scholarly literature is being produced has been increasing
at approximately 3.5 percent per year for decades. This means that during a
typical 40 year career the amount of new literature produced each year
increases by a factor of four. The methods scholars use to discover relevant
literature must change. Just like everybody else involved in information
discovery, scholars are confronted with information overload. Two decades ago,
this discovery process essentially consisted of paging through abstract books,
talking to colleagues and librarians, and browsing journals. A time-consuming
process, which could even be longer if material had to be shipped from
elsewhere. Now much of this discovery process is mediated by online scholarly
information systems. All these systems are relatively new, and all are still
changing. They all share a common goal: to provide their users with access to
the literature relevant to their specific needs. To achieve this each system
responds to actions by the user by displaying articles which the system judges
relevant to the user's current needs. Recently search systems which use
particularly sophisticated methodologies to recommend a few specific papers to
the user have been called "recommender systems". These methods are in line with
the current use of the term "recommender system" in computer science. We do not
adopt this definition, rather we view systems like these as components in a
larger whole, which is presented by the scholarly information systems
themselves. In what follows we view the recommender system as an aspect of the
entire information system; one which combines the massive memory capacities of
the machine with the cognitive abilities of the human user to achieve a
human-machine synergy.Comment: 14 pages, part of the forthcoming MIT book "Bibliometrics and Beyond:
Metrics-Based Evaluation of Scholarly Research" edited by Blaise Cronin and
Cassidy R. Sugimot
Recommender Systems in E-commerce
E-commerce recommender systems are becoming increasingly important in the
current digital world. They are used to personalize user experience, help
customers find what they need quickly and efficiently, and increase revenue for
the business. However, there are several challenges associated with big
data-based e-commerce recommender systems. These challenges include limited
resources, data validity period, cold start, long tail problem, scalability. In
this paper, we discuss the challenges and potential solutions to overcome these
challenges. We also discuss the different types of e-commerce recommender
systems, their advantages, and disadvantages. We conclude with some future
research directions to improve the performance of e-commerce recommender
systems
Recommender Systems for Scientific and Technical Information Providers
Providers of scientific and technical information are a promising application area of recommender systems due to high search costs for their goods and the general problem of assessing the quality of information products. Nevertheless, the usage of recommendation services in this market is still in its infancy. This book presents economical concepts,
statistical methods and algorithms, technical architectures, as well as experiences from case studies on how recommender systems can be integrated
- …