42,756 research outputs found
Multi-Target Prediction: A Unifying View on Problems and Methods
Multi-target prediction (MTP) is concerned with the simultaneous prediction
of multiple target variables of diverse type. Due to its enormous application
potential, it has developed into an active and rapidly expanding research field
that combines several subfields of machine learning, including multivariate
regression, multi-label classification, multi-task learning, dyadic prediction,
zero-shot learning, network inference, and matrix completion. In this paper, we
present a unifying view on MTP problems and methods. First, we formally discuss
commonalities and differences between existing MTP problems. To this end, we
introduce a general framework that covers the above subfields as special cases.
As a second contribution, we provide a structured overview of MTP methods. This
is accomplished by identifying a number of key properties, which distinguish
such methods and determine their suitability for different types of problems.
Finally, we also discuss a few challenges for future research
Scalable Bayesian modeling, monitoring and analysis of dynamic network flow data
Traffic flow count data in networks arise in many applications, such as
automobile or aviation transportation, certain directed social network
contexts, and Internet studies. Using an example of Internet browser traffic
flow through site-segments of an international news website, we present
Bayesian analyses of two linked classes of models which, in tandem, allow fast,
scalable and interpretable Bayesian inference. We first develop flexible
state-space models for streaming count data, able to adaptively characterize
and quantify network dynamics efficiently in real-time. We then use these
models as emulators of more structured, time-varying gravity models that allow
formal dissection of network dynamics. This yields interpretable inferences on
traffic flow characteristics, and on dynamics in interactions among network
nodes. Bayesian monitoring theory defines a strategy for sequential model
assessment and adaptation in cases when network flow data deviates from
model-based predictions. Exploratory and sequential monitoring analyses of
evolving traffic on a network of web site-segments in e-commerce demonstrate
the utility of this coupled Bayesian emulation approach to analysis of
streaming network count data.Comment: 29 pages, 16 figure
Beyond Classification: Latent User Interests Profiling from Visual Contents Analysis
User preference profiling is an important task in modern online social
networks (OSN). With the proliferation of image-centric social platforms, such
as Pinterest, visual contents have become one of the most informative data
streams for understanding user preferences. Traditional approaches usually
treat visual content analysis as a general classification problem where one or
more labels are assigned to each image. Although such an approach simplifies
the process of image analysis, it misses the rich context and visual cues that
play an important role in people's perception of images. In this paper, we
explore the possibilities of learning a user's latent visual preferences
directly from image contents. We propose a distance metric learning method
based on Deep Convolutional Neural Networks (CNN) to directly extract
similarity information from visual contents and use the derived distance metric
to mine individual users' fine-grained visual preferences. Through our
preliminary experiments using data from 5,790 Pinterest users, we show that
even for the images within the same category, each user possesses distinct and
individually-identifiable visual preferences that are consistent over their
lifetime. Our results underscore the untapped potential of finer-grained visual
preference profiling in understanding users' preferences.Comment: 2015 IEEE 15th International Conference on Data Mining Workshop
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