4,371 research outputs found
Supervised Rank Aggregation for Predicting Influence in Networks
Much work in Social Network Analysis has focused on the identification of the
most important actors in a social network. This has resulted in several
measures of influence and authority. While most of such sociometrics (e.g.,
PageRank) are driven by intuitions based on an actors location in a network,
asking for the "most influential" actors in itself is an ill-posed question,
unless it is put in context with a specific measurable task. Constructing a
predictive task of interest in a given domain provides a mechanism to
quantitatively compare different measures of influence. Furthermore, when we
know what type of actionable insight to gather, we need not rely on a single
network centrality measure. A combination of measures is more likely to capture
various aspects of the social network that are predictive and beneficial for
the task. Towards this end, we propose an approach to supervised rank
aggregation, driven by techniques from Social Choice Theory. We illustrate the
effectiveness of this method through experiments on Twitter and citation
networks
GhostLink: Latent Network Inference for Influence-aware Recommendation
Social influence plays a vital role in shaping a user's behavior in online
communities dealing with items of fine taste like movies, food, and beer. For
online recommendation, this implies that users' preferences and ratings are
influenced due to other individuals. Given only time-stamped reviews of users,
can we find out who-influences-whom, and characteristics of the underlying
influence network? Can we use this network to improve recommendation?
While prior works in social-aware recommendation have leveraged social
interaction by considering the observed social network of users, many
communities like Amazon, Beeradvocate, and Ratebeer do not have explicit
user-user links. Therefore, we propose GhostLink, an unsupervised probabilistic
graphical model, to automatically learn the latent influence network underlying
a review community -- given only the temporal traces (timestamps) of users'
posts and their content. Based on extensive experiments with four real-world
datasets with 13 million reviews, we show that GhostLink improves item
recommendation by around 23% over state-of-the-art methods that do not consider
this influence. As additional use-cases, we show that GhostLink can be used to
differentiate between users' latent preferences and influenced ones, as well as
to detect influential users based on the learned influence graph
Learning and Optimization with Submodular Functions
In many naturally occurring optimization problems one needs to ensure that
the definition of the optimization problem lends itself to solutions that are
tractable to compute. In cases where exact solutions cannot be computed
tractably, it is beneficial to have strong guarantees on the tractable
approximate solutions. In order operate under these criterion most optimization
problems are cast under the umbrella of convexity or submodularity. In this
report we will study design and optimization over a common class of functions
called submodular functions. Set functions, and specifically submodular set
functions, characterize a wide variety of naturally occurring optimization
problems, and the property of submodularity of set functions has deep
theoretical consequences with wide ranging applications. Informally, the
property of submodularity of set functions concerns the intuitive "principle of
diminishing returns. This property states that adding an element to a smaller
set has more value than adding it to a larger set. Common examples of
submodular monotone functions are entropies, concave functions of cardinality,
and matroid rank functions; non-monotone examples include graph cuts, network
flows, and mutual information.
In this paper we will review the formal definition of submodularity; the
optimization of submodular functions, both maximization and minimization; and
finally discuss some applications in relation to learning and reasoning using
submodular functions.Comment: Tech Report - USC Computer Science CS-599, Convex and Combinatorial
Optimizatio
Recommendation Systems for Tourism Based on Social Networks: A Survey
Nowadays, recommender systems are present in many daily activities such as
online shopping, browsing social networks, etc. Given the rising demand for
reinvigoration of the tourist industry through information technology,
recommenders have been included into tourism websites such as Expedia, Booking
or Tripadvisor, among others. Furthermore, the amount of scientific papers
related to recommender systems for tourism is on solid and continuous growth
since 2004. Much of this growth is due to social networks that, besides to
offer researchers the possibility of using a great mass of available and
constantly updated data, they also enable the recommendation systems to become
more personalised, effective and natural. This paper reviews and analyses many
research publications focusing on tourism recommender systems that use social
networks in their projects. We detail their main characteristics, like which
social networks are exploited, which data is extracted, the applied
recommendation techniques, the methods of evaluation, etc. Through a
comprehensive literature review, we aim to collaborate with the future
recommender systems, by giving some clear classifications and descriptions of
the current tourism recommender systems
Data-driven Computational Social Science: A Survey
Social science concerns issues on individuals, relationships, and the whole
society. The complexity of research topics in social science makes it the
amalgamation of multiple disciplines, such as economics, political science, and
sociology, etc. For centuries, scientists have conducted many studies to
understand the mechanisms of the society. However, due to the limitations of
traditional research methods, there exist many critical social issues to be
explored. To solve those issues, computational social science emerges due to
the rapid advancements of computation technologies and the profound studies on
social science. With the aids of the advanced research techniques, various
kinds of data from diverse areas can be acquired nowadays, and they can help us
look into social problems with a new eye. As a result, utilizing various data
to reveal issues derived from computational social science area has attracted
more and more attentions. In this paper, to the best of our knowledge, we
present a survey on data-driven computational social science for the first time
which primarily focuses on reviewing application domains involving human
dynamics. The state-of-the-art research on human dynamics is reviewed from
three aspects: individuals, relationships, and collectives. Specifically, the
research methodologies used to address research challenges in aforementioned
application domains are summarized. In addition, some important open challenges
with respect to both emerging research topics and research methods are
discussed.Comment: 28 pages, 8 figure
Modeling Influence with Semantics in Social Networks: a Survey
The discovery of influential entities in all kinds of networks (e.g. social,
digital, or computer) has always been an important field of study. In recent
years, Online Social Networks (OSNs) have been established as a basic means of
communication and often influencers and opinion makers promote politics,
events, brands or products through viral content. In this work, we present a
systematic review across i) online social influence metrics, properties, and
applications and ii) the role of semantic in modeling OSNs information. We end
up with the conclusion that both areas can jointly provide useful insights
towards the qualitative assessment of viral user-generated content, as well as
for modeling the dynamic properties of influential content and its flow
dynamics.Comment: 61 pages, 3 figures, 4 table
Social Science Guided Feature Engineering: A Novel Approach to Signed Link Analysis
Many real-world relations can be represented by signed networks with positive
links (e.g., friendships and trust) and negative links (e.g., foes and
distrust). Link prediction helps advance tasks in social network analysis such
as recommendation systems. Most existing work on link analysis focuses on
unsigned social networks. The existence of negative links piques research
interests in investigating whether properties and principles of signed networks
differ from those of unsigned networks, and mandates dedicated efforts on link
analysis for signed social networks. Recent findings suggest that properties of
signed networks substantially differ from those of unsigned networks and
negative links can be of significant help in signed link analysis in
complementary ways. In this article, we center our discussion on a challenging
problem of signed link analysis. Signed link analysis faces the problem of data
sparsity, i.e. only a small percentage of signed links are given. This problem
can even get worse when negative links are much sparser than positive ones as
users are inclined more towards positive disposition rather than negative. We
investigate how we can take advantage of other sources of information for
signed link analysis. This research is mainly guided by three social science
theories, Emotional Information, Diffusion of Innovations, and Individual
Personality. Guided by these, we extract three categories of related features
and leverage them for signed link analysis. Experiments show the significance
of the features gleaned from social theories for signed link prediction and
addressing the data sparsity challenge.Comment: This worked is published at ACM Transactions on Intelligent Systems
and Technology(ACM TIST), 201
Using Sentiment Representation Learning to Enhance Gender Classification for User Profiling
User profiling means exploiting the technology of machine learning to predict
attributes of users, such as demographic attributes, hobby attributes,
preference attributes, etc. It's a powerful data support of precision
marketing. Existing methods mainly study network behavior, personal
preferences, post texts to build user profile. Through our data analysis of
micro-blog, we find that females show more positive and have richer emotions
than males in online social platform. This difference is very conducive to the
distinction between genders. Therefore, we argue that sentiment context is
important as well for user profiling.This paper focuses on exploiting microblog
user posts to predict one of the demographic labels: gender. We propose a
Sentiment Representation Learning based Multi-Layer Perceptron(SRL-MLP) model
to classify gender. First we build a sentiment polarity classifier in advance
by training Long Short-Term Memory(LSTM) model on e-commerce review corpus.
Next we transfer sentiment representation to a basic MLP network. Last we
conduct experiments on gender classification by sentiment representation.
Experimental results show that our approach can improve gender classification
accuracy by 5.53\%, from 84.20\% to 89.73\%
Mobile Information Retrieval
Mobile Information Retrieval (Mobile IR) is a relatively recent branch of
Information Retrieval (IR) that is concerned with enabling users to carry out,
using a mobile device, all the classical IR operations that they were used to
carry out on a desktop. This includes finding content available on local
repositories or on the web in response to a user query, interacting with the
system in an explicit or implicit way, reformulate the query and/or visualise
the content of the retrieved documents, as well as providing relevance
judgments to improve the retrieval process.
This book is structured as follows. Chapter 2 provides a very brief overview
of IR and of Mobile IR, briefly outlining what in Mobile IR is different from
IR. Chapter 3 provides the foundations of Mobile IR, looking at the
characteristics of mobile devices and what they bring to IR, but also looking
at how the concept of relevance changed from standard IR to Mobile IR. Chapter
4 presents an overview of the document collections that are searchable by a
Mobile IR system, and that are somehow different from classical IR ones;
available for experimentation, including collections of data that have become
complementary to Mobile IR. Similarly, Chapter 5 reviews mobile information
needs studies and users log analysis. Chapter 6 reviews studies aimed at
adapting and improving the users interface to the needs of Mobile IR. Chapter
7, instead, reviews work on context awareness, which studies the many aspects
of the user context that Mobile IR employs. Chapter 8 reviews some of
evaluation work done in Mobile IR, highlighting the distinctions with classical
IR from the perspectives of two main IR evaluation methodologies: users studies
and test collections. Finally, Chapter 9 reports the conclusions of this
review, highlighting briefly some trends in Mobile IR that we believe will
drive research in the next few years.Comment: 116 pages, published in 201
Socially-Aware Networking: A Survey
The widespread proliferation of handheld devices enables mobile carriers to
be connected at anytime and anywhere. Meanwhile, the mobility patterns of
mobile devices strongly depend on the users' movements, which are closely
related to their social relationships and behaviors. Consequently, today's
mobile networks are becoming increasingly human centric. This leads to the
emergence of a new field which we call socially-aware networking (SAN). One of
the major features of SAN is that social awareness becomes indispensable
information for the design of networking solutions. This emerging paradigm is
applicable to various types of networks (e.g. opportunistic networks, mobile
social networks, delay tolerant networks, ad hoc networks, etc) where the users
have social relationships and interactions. By exploiting social properties of
nodes, SAN can provide better networking support to innovative applications and
services. In addition, it facilitates the convergence of human society and
cyber physical systems. In this paper, for the first time, to the best of our
knowledge, we present a survey of this emerging field. Basic concepts of SAN
are introduced. We intend to generalize the widely-used social properties in
this regard. The state-of-the-art research on SAN is reviewed with focus on
three aspects: routing and forwarding, incentive mechanisms and data
dissemination. Some important open issues with respect to mobile social sensing
and learning, privacy, node selfishness and scalability are discussed.Comment: accepted. IEEE Systems Journal, 201
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