6,818 research outputs found
Customer purchase behavior prediction in E-commerce: a conceptual framework and research agenda
Digital retailers are experiencing an increasing number of transactions coming from their consumers online, a consequence of the convenience in buying goods via E-commerce platforms. Such interactions compose complex behavioral patterns which can be analyzed through predictive analytics to enable businesses to understand consumer needs. In this abundance of big data and possible tools to analyze them, a systematic review of the literature is missing. Therefore, this paper presents a systematic literature review of recent research dealing with customer purchase prediction in the E-commerce context. The main contributions are a novel analytical framework and a research agenda in the field. The framework reveals three main tasks in this review, namely, the prediction of customer intents, buying sessions, and purchase decisions. Those are followed by their employed predictive methodologies and are analyzed from three perspectives. Finally, the research agenda provides major existing issues for further research in the field of purchase behavior prediction online
Advances in quantum machine learning
Here we discuss advances in the field of quantum machine learning. The
following document offers a hybrid discussion; both reviewing the field as it
is currently, and suggesting directions for further research. We include both
algorithms and experimental implementations in the discussion. The field's
outlook is generally positive, showing significant promise. However, we believe
there are appreciable hurdles to overcome before one can claim that it is a
primary application of quantum computation.Comment: 38 pages, 17 Figure
Numeric Input Relations for Relational Learning with Applications to Community Structure Analysis
Most work in the area of statistical relational learning (SRL) is focussed on
discrete data, even though a few approaches for hybrid SRL models have been
proposed that combine numerical and discrete variables. In this paper we
distinguish numerical random variables for which a probability distribution is
defined by the model from numerical input variables that are only used for
conditioning the distribution of discrete response variables. We show how
numerical input relations can very easily be used in the Relational Bayesian
Network framework, and that existing inference and learning methods need only
minor adjustments to be applied in this generalized setting. The resulting
framework provides natural relational extensions of classical probabilistic
models for categorical data. We demonstrate the usefulness of RBN models with
numeric input relations by several examples.
In particular, we use the augmented RBN framework to define probabilistic
models for multi-relational (social) networks in which the probability of a
link between two nodes depends on numeric latent feature vectors associated
with the nodes. A generic learning procedure can be used to obtain a
maximum-likelihood fit of model parameters and latent feature values for a
variety of models that can be expressed in the high-level RBN representation.
Specifically, we propose a model that allows us to interpret learned latent
feature values as community centrality degrees by which we can identify nodes
that are central for one community, that are hubs between communities, or that
are isolated nodes. In a multi-relational setting, the model also provides a
characterization of how different relations are associated with each community
Bibliographic Analysis on Research Publications using Authors, Categorical Labels and the Citation Network
Bibliographic analysis considers the author's research areas, the citation
network and the paper content among other things. In this paper, we combine
these three in a topic model that produces a bibliographic model of authors,
topics and documents, using a nonparametric extension of a combination of the
Poisson mixed-topic link model and the author-topic model. This gives rise to
the Citation Network Topic Model (CNTM). We propose a novel and efficient
inference algorithm for the CNTM to explore subsets of research publications
from CiteSeerX. The publication datasets are organised into three corpora,
totalling to about 168k publications with about 62k authors. The queried
datasets are made available online. In three publicly available corpora in
addition to the queried datasets, our proposed model demonstrates an improved
performance in both model fitting and document clustering, compared to several
baselines. Moreover, our model allows extraction of additional useful knowledge
from the corpora, such as the visualisation of the author-topics network.
Additionally, we propose a simple method to incorporate supervision into topic
modelling to achieve further improvement on the clustering task.Comment: Preprint for Journal Machine Learnin
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