37,033 research outputs found
Vista: A Visually, Socially, and Temporally-aware Model for Artistic Recommendation
Understanding users' interactions with highly subjective content---like
artistic images---is challenging due to the complex semantics that guide our
preferences. On the one hand one has to overcome `standard' recommender systems
challenges, such as dealing with large, sparse, and long-tailed datasets. On
the other, several new challenges present themselves, such as the need to model
content in terms of its visual appearance, or even social dynamics, such as a
preference toward a particular artist that is independent of the art they
create.
In this paper we build large-scale recommender systems to model the dynamics
of a vibrant digital art community, Behance, consisting of tens of millions of
interactions (clicks and `appreciates') of users toward digital art.
Methodologically, our main contributions are to model (a) rich content,
especially in terms of its visual appearance; (b) temporal dynamics, in terms
of how users prefer `visually consistent' content within and across sessions;
and (c) social dynamics, in terms of how users exhibit preferences both towards
certain art styles, as well as the artists themselves.Comment: 8 pages, 3 figure
A Class of Temporal Hierarchical Exponential Random Graph Models for Longitudinal Network Data
As a representation of relational data over time series, longitudinal
networks provide opportunities to study link formation processes. However,
networks at scale often exhibits community structure (i.e. clustering), which
may confound local structural effects if it is not considered appropriately in
statistical analysis. To infer the (possibly) evolving clusters and other
network structures (e.g. degree distribution and/or transitivity) within each
community, simultaneously, we propose a class of statistical models named
Temporal Hierarchical Exponential Random Graph Models (THERGM). Our generative
model imposes a Markovian transition matrix for nodes to change their
membership, and assumes they join new community in a preferential attachment
way. For those remaining in the same cluster, they follow a specific temporal
ERG model (TERGM). While a direct MCMC based Bayesian estimation is
computational infeasible, we propose a two-stage strategy. At the first stage,
a specific dynamic latent space model will be used as the working model for
clustering. At the second stage, estimated memberships are taken as given to
fit a TERG model in each cluster. We evaluate our methods on simulated data in
terms of the mis-clustering rate, as well as the goodness of fit and link
prediction accuracy
Community Specific Temporal Topic Discovery from Social Media
Studying temporal dynamics of topics in social media is very useful to
understand online user behaviors. Most of the existing work on this subject
usually monitors the global trends, ignoring variation among communities. Since
users from different communities tend to have varying tastes and interests,
capturing community-level temporal change can improve the understanding and
management of social content. Additionally, it can further facilitate the
applications such as community discovery, temporal prediction and online
marketing. However, this kind of extraction becomes challenging due to the
intricate interactions between community and topic, and intractable
computational complexity.
In this paper, we take a unified solution towards the community-level topic
dynamic extraction. A probabilistic model, CosTot (Community Specific
Topics-over-Time) is proposed to uncover the hidden topics and communities, as
well as capture community-specific temporal dynamics. Specifically, CosTot
considers text, time, and network information simultaneously, and well
discovers the interactions between community and topic over time. We then
discuss the approximate inference implementation to enable scalable computation
of model parameters, especially for large social data. Based on this, the
application layer support for multi-scale temporal analysis and community
exploration is also investigated.
We conduct extensive experimental studies on a large real microblog dataset,
and demonstrate the superiority of proposed model on tasks of time stamp
prediction, link prediction and topic perplexity.Comment: 12 pages, 16 figures, submitted to VLDB 201
Improving Latent User Models in Online Social Media
Modern social platforms are characterized by the presence of rich
user-behavior data associated with the publication, sharing and consumption of
textual content. Users interact with content and with each other in a complex
and dynamic social environment while simultaneously evolving over time. In
order to effectively characterize users and predict their future behavior in
such a setting, it is necessary to overcome several challenges. Content
heterogeneity and temporal inconsistency of behavior data result in severe
sparsity at the user level. In this paper, we propose a novel
mutual-enhancement framework to simultaneously partition and learn latent
activity profiles of users. We propose a flexible user partitioning approach to
effectively discover rare behaviors and tackle user-level sparsity. We
extensively evaluate the proposed framework on massive datasets from real-world
platforms including Q&A networks and interactive online courses (MOOCs). Our
results indicate significant gains over state-of-the-art behavior models ( 15%
avg ) in a varied range of tasks and our gains are further magnified for users
with limited interaction data. The proposed algorithms are amenable to
parallelization, scale linearly in the size of datasets, and provide
flexibility to model diverse facets of user behavior
Understanding Urban Dynamics via Context-aware Tensor Factorization with Neighboring Regularization
Recent years have witnessed the world-wide emergence of mega-metropolises
with incredibly huge populations. Understanding residents mobility patterns, or
urban dynamics, thus becomes crucial for building modern smart cities. In this
paper, we propose a Neighbor-Regularized and context-aware Non-negative Tensor
Factorization model (NR-cNTF) to discover interpretable urban dynamics from
urban heterogeneous data. Different from many existing studies concerned with
prediction tasks via tensor completion, NR-cNTF focuses on gaining urban
managerial insights from spatial, temporal, and spatio-temporal patterns. This
is enabled by high-quality Tucker factorizations regularized by both POI-based
urban contexts and geographically neighboring relations. NR-cNTF is also
capable of unveiling long-term evolutions of urban dynamics via a pipeline
initialization approach. We apply NR-cNTF to a real-life data set containing
rich taxi GPS trajectories and POI records of Beijing. The results indicate: 1)
NR-cNTF accurately captures four kinds of city rhythms and seventeen spatial
communities; 2) the rapid development of Beijing, epitomized by the CBD area,
indeed intensifies the job-housing imbalance; 3) the southern areas with recent
government investments have shown more healthy development tendency. Finally,
NR-cNTF is compared with some baselines on traffic prediction, which further
justifies the importance of urban contexts awareness and neighboring
regulations
A Review of Dynamic Network Models with Latent Variables
We present a selective review of statistical modeling of dynamic networks. We
focus on models with latent variables, specifically, the latent space models
and the latent class models (or stochastic blockmodels), which investigate both
the observed features and the unobserved structure of networks. We begin with
an overview of the static models, and then we introduce the dynamic extensions.
For each dynamic model, we also discuss its applications that have been studied
in the literature, with the data source listed in Appendix. Based on the
review, we summarize a list of open problems and challenges in dynamic network
modeling with latent variables
Modeling Implicit Communities using Spatio-Temporal Point Processes from Geo-tagged Event Traces
The location check-ins of users through various location-based services such
as Foursquare, Twitter, and Facebook Places, etc., generate large traces of
geo-tagged events. These event-traces often manifest in hidden (possibly
overlapping) communities of users with similar interests. Inferring these
implicit communities is crucial for forming user profiles for improvements in
recommendation and prediction tasks. Given only time-stamped geo-tagged traces
of users, can we find out these implicit communities, and characteristics of
the underlying influence network? Can we use this network to improve the next
location prediction task? In this paper, we focus on the problem of community
detection as well as capturing the underlying diffusion process and propose a
model COLAB based on Spatio-temporal point processes in continuous time but
discrete space of locations that simultaneously models the implicit communities
of users based on their check-in activities, without making use of their social
network connections. COLAB captures the semantic features of the location,
user-to-user influence along with spatial and temporal preferences of users. To
learn the latent community of users and model parameters, we propose an
algorithm based on stochastic variational inference. To the best of our
knowledge, this is the first attempt at jointly modeling the diffusion process
with activity-driven implicit communities. We demonstrate COLAB achieves up to
27% improvements in location prediction task over recent deep point-process
based methods on geo-tagged event traces collected from Foursquare check-ins.Comment: 17 page
Comparison of Deep Neural Networks and Deep Hierarchical Models for Spatio-Temporal Data
Spatio-temporal data are ubiquitous in the agricultural, ecological, and
environmental sciences, and their study is important for understanding and
predicting a wide variety of processes. One of the difficulties with modeling
spatial processes that change in time is the complexity of the dependence
structures that must describe how such a process varies, and the presence of
high-dimensional complex data sets and large prediction domains. It is
particularly challenging to specify parameterizations for nonlinear dynamic
spatio-temporal models (DSTMs) that are simultaneously useful scientifically
and efficient computationally. Statisticians have developed deep hierarchical
models that can accommodate process complexity as well as the uncertainties in
the predictions and inference. However, these models can be expensive and are
typically application specific. On the other hand, the machine learning
community has developed alternative "deep learning" approaches for nonlinear
spatio-temporal modeling. These models are flexible yet are typically not
implemented in a probabilistic framework. The two paradigms have many things in
common and suggest hybrid approaches that can benefit from elements of each
framework. This overview paper presents a brief introduction to the deep
hierarchical DSTM (DH-DSTM) framework, and deep models in machine learning,
culminating with the deep neural DSTM (DN-DSTM). Recent approaches that combine
elements from DH-DSTMs and echo state network DN-DSTMs are presented as
illustrations.Comment: 26 pages, including 6 figures and reference
Change Surfaces for Expressive Multidimensional Changepoints and Counterfactual Prediction
Identifying changes in model parameters is fundamental in machine learning
and statistics. However, standard changepoint models are limited in
expressiveness, often addressing unidimensional problems and assuming
instantaneous changes. We introduce change surfaces as a multidimensional and
highly expressive generalization of changepoints. We provide a model-agnostic
formalization of change surfaces, illustrating how they can provide variable,
heterogeneous, and non-monotonic rates of change across multiple dimensions.
Additionally, we show how change surfaces can be used for counterfactual
prediction. As a concrete instantiation of the change surface framework, we
develop Gaussian Process Change Surfaces (GPCS). We demonstrate counterfactual
prediction with Bayesian posterior mean and credible sets, as well as massive
scalability by introducing novel methods for additive non-separable kernels.
Using two large spatio-temporal datasets we employ GPCS to discover and
characterize complex changes that can provide scientific and policy relevant
insights. Specifically, we analyze twentieth century measles incidence across
the United States and discover previously unknown heterogeneous changes after
the introduction of the measles vaccine. Additionally, we apply the model to
requests for lead testing kits in New York City, discovering distinct spatial
and demographic patterns
Mixed Effects Modeling for Areal Data that Exhibit Multivariate-Spatio-Temporal Dependencies
There are many data sources available that report related variables of
interest that are also referenced over geographic regions and time; however,
there are relatively few general statistical methods that one can readily use
that incorporate these multivariate-spatio-temporal dependencies. As such, we
introduce the multivariate-spatio-temporal mixed effects model (MSTM) to
analyze areal data with multivariate-spatio-temporal dependencies. The proposed
MSTM extends the notion of Moran's I basis functions to the
multivariate-spatio-temporal setting. This extension leads to several
methodological contributions including extremely effective dimension reduction,
a dynamic linear model for multivariate-spatio-temporal areal processes, and
the reduction of a high-dimensional parameter space using a novel parameter
model. Several examples are used to demonstrate that the MSTM provides an
extremely viable solution to many important problems found in different and
distinct corners of the spatio-temporal statistics literature including:
modeling nonseparable and nonstationary covariances, combing data from multiple
repeated surveys, and analyzing massive multivariate-spatio-temporal datasets
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