3,384 research outputs found
Dependency Networks for Collaborative Filtering and Data Visualization
We describe a graphical model for probabilistic relationships---an
alternative to the Bayesian network---called a dependency network. The graph of
a dependency network, unlike a Bayesian network, is potentially cyclic. The
probability component of a dependency network, like a Bayesian network, is a
set of conditional distributions, one for each node given its parents. We
identify several basic properties of this representation and describe a
computationally efficient procedure for learning the graph and probability
components from data. We describe the application of this representation to
probabilistic inference, collaborative filtering (the task of predicting
preferences), and the visualization of acausal predictive relationships.Comment: Appears in Proceedings of the Sixteenth Conference on Uncertainty in
Artificial Intelligence (UAI2000
Collaborative filtering via sparse Markov random fields
Recommender systems play a central role in providing individualized access to
information and services. This paper focuses on collaborative filtering, an
approach that exploits the shared structure among mind-liked users and similar
items. In particular, we focus on a formal probabilistic framework known as
Markov random fields (MRF). We address the open problem of structure learning
and introduce a sparsity-inducing algorithm to automatically estimate the
interaction structures between users and between items. Item-item and user-user
correlation networks are obtained as a by-product. Large-scale experiments on
movie recommendation and date matching datasets demonstrate the power of the
proposed method
Coresets for Dependency Networks
Many applications infer the structure of a probabilistic graphical model from
data to elucidate the relationships between variables. But how can we train
graphical models on a massive data set? In this paper, we show how to construct
coresets -compressed data sets which can be used as proxy for the original data
and have provably bounded worst case error- for Gaussian dependency networks
(DNs), i.e., cyclic directed graphical models over Gaussians, where the parents
of each variable are its Markov blanket. Specifically, we prove that Gaussian
DNs admit coresets of size independent of the size of the data set.
Unfortunately, this does not extend to DNs over members of the exponential
family in general. As we will prove, Poisson DNs do not admit small coresets.
Despite this worst-case result, we will provide an argument why our coreset
construction for DNs can still work well in practice on count data. To
corroborate our theoretical results, we empirically evaluated the resulting
Core DNs on real data sets. The resultsComment: 16 pages, 3 figure
Using Temporal Data for Making Recommendations
We treat collaborative filtering as a univariate time series estimation
problem: given a user's previous votes, predict the next vote. We describe two
families of methods for transforming data to encode time order in ways amenable
to off-the-shelf classification and density estimation tools, and examine the
results of using these approaches on several real-world data sets. The
improvements in predictive accuracy we realize recommend the use of other
predictive algorithms that exploit the temporal order of data.Comment: Appears in Proceedings of the Seventeenth Conference on Uncertainty
in Artificial Intelligence (UAI2001
Collaborative Ensemble Learning: Combining Collaborative and Content-Based Information Filtering via Hierarchical Bayes
Collaborative filtering (CF) and content-based filtering (CBF) have widely
been used in information filtering applications. Both approaches have their
strengths and weaknesses which is why researchers have developed hybrid
systems. This paper proposes a novel approach to unify CF and CBF in a
probabilistic framework, named collaborative ensemble learning. It uses
probabilistic SVMs to model each user's profile (as CBF does).At the prediction
phase, it combines a society OF users profiles, represented by their respective
SVM models, to predict an active users preferences(the CF idea).The combination
scheme is embedded in a probabilistic framework and retains an intuitive
explanation.Moreover, collaborative ensemble learning does not require a global
training stage and thus can incrementally incorporate new data.We report
results based on two data sets. For the Reuters-21578 text data set, we
simulate user ratings under the assumption that each user is interested in only
one category. In the second experiment, we use users' opinions on a set of 642
art images that were collected through a web-based survey. For both data sets,
collaborative ensemble achieved excellent performance in terms of
recommendation accuracy.Comment: Appears in Proceedings of the Nineteenth Conference on Uncertainty in
Artificial Intelligence (UAI2003
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
Heterogeneous Multi-Layered Network Model for Omics Data Integration and Analysis
Advances in next-generation sequencing and high-throughput techniques have enabled the generation of vast amounts of diverse omics data. These big data provide an unprecedented opportunity in biology, but impose great challenges in data integration, data mining, and knowledge discovery due to the complexity, heterogeneity, dynamics, uncertainty, and high-dimensionality inherited in the omics data. Network has been widely used to represent relations between entities in biological system, such as protein-protein interaction, gene regulation, and brain connectivity (i.e. network construction) as well as to infer novel relations given a reconstructed network (aka link prediction). Particularly, heterogeneous multi-layered network (HMLN) has proven successful in integrating diverse biological data for the representation of the hierarchy of biological system. The HMLN provides unparalleled opportunities but imposes new computational challenges on establishing causal genotype-phenotype associations and understanding environmental impact on organisms. In this review, we focus on the recent advances in developing novel computational methods for the inference of novel biological relations from the HMLN. We first discuss the properties of biological HMLN. Then we survey four categories of state-of-the-art methods (matrix factorization, random walk, knowledge graph, and deep learning). Thirdly, we demonstrate their applications to omics data integration and analysis. Finally, we outline strategies for future directions in the development of new HMLN models
NSML: Meet the MLaaS platform with a real-world case study
The boom of deep learning induced many industries and academies to introduce
machine learning based approaches into their concern, competitively. However,
existing machine learning frameworks are limited to sufficiently fulfill the
collaboration and management for both data and models. We proposed NSML, a
machine learning as a service (MLaaS) platform, to meet these demands. NSML
helps machine learning work be easily launched on a NSML cluster and provides a
collaborative environment which can afford development at enterprise scale.
Finally, NSML users can deploy their own commercial services with NSML cluster.
In addition, NSML furnishes convenient visualization tools which assist the
users in analyzing their work. To verify the usefulness and accessibility of
NSML, we performed some experiments with common examples. Furthermore, we
examined the collaborative advantages of NSML through three competitions with
real-world use cases
Hierarchical Context enabled Recurrent Neural Network for Recommendation
A long user history inevitably reflects the transitions of personal interests
over time. The analyses on the user history require the robust sequential model
to anticipate the transitions and the decays of user interests. The user
history is often modeled by various RNN structures, but the RNN structures in
the recommendation system still suffer from the long-term dependency and the
interest drifts. To resolve these challenges, we suggest HCRNN with three
hierarchical contexts of the global, the local, and the temporary interests.
This structure is designed to withhold the global long-term interest of users,
to reflect the local sub-sequence interests, and to attend the temporary
interests of each transition. Besides, we propose a hierarchical context-based
gate structure to incorporate our \textit{interest drift assumption}. As we
suggest a new RNN structure, we support HCRNN with a complementary
\textit{bi-channel attention} structure to utilize hierarchical context. We
experimented the suggested structure on the sequential recommendation tasks
with CiteULike, MovieLens, and LastFM, and our model showed the best
performances in the sequential recommendations
Computational Models for Attitude and Actions Prediction
In this paper, we present computational models to predict Twitter users'
attitude towards a specific brand through their personal and social
characteristics. We also predict their likelihood to take different actions
based on their attitudes. In order to operationalize our research on users'
attitude and actions, we collected ground-truth data through surveys of Twitter
users. We have conducted experiments using two real world datasets to validate
the effectiveness of our attitude and action prediction framework. Finally, we
show how our models can be integrated with a visual analytics system for
customer intervention.Comment: This is an extended version of a previously published IUI 2016 paper
from same authors. http://dl.acm.org/citation.cfm?id=285680
- …