15,706 research outputs found
Interaction-aware Factorization Machines for Recommender Systems
Factorization Machine (FM) is a widely used supervised learning approach by
effectively modeling of feature interactions. Despite the successful
application of FM and its many deep learning variants, treating every feature
interaction fairly may degrade the performance. For example, the interactions
of a useless feature may introduce noises; the importance of a feature may also
differ when interacting with different features. In this work, we propose a
novel model named \emph{Interaction-aware Factorization Machine} (IFM) by
introducing Interaction-Aware Mechanism (IAM), which comprises the
\emph{feature aspect} and the \emph{field aspect}, to learn flexible
interactions on two levels. The feature aspect learns feature interaction
importance via an attention network while the field aspect learns the feature
interaction effect as a parametric similarity of the feature interaction vector
and the corresponding field interaction prototype. IFM introduces more
structured control and learns feature interaction importance in a stratified
manner, which allows for more leverage in tweaking the interactions on both
feature-wise and field-wise levels. Besides, we give a more generalized
architecture and propose Interaction-aware Neural Network (INN) and DeepIFM to
capture higher-order interactions. To further improve both the performance and
efficiency of IFM, a sampling scheme is developed to select interactions based
on the field aspect importance. The experimental results from two well-known
datasets show the superiority of the proposed models over the state-of-the-art
methods
Scalable Audience Reach Estimation in Real-time Online Advertising
Online advertising has been introduced as one of the most efficient methods
of advertising throughout the recent years. Yet, advertisers are concerned
about the efficiency of their online advertising campaigns and consequently,
would like to restrict their ad impressions to certain websites and/or certain
groups of audience. These restrictions, known as targeting criteria, limit the
reachability for better performance. This trade-off between reachability and
performance illustrates a need for a forecasting system that can quickly
predict/estimate (with good accuracy) this trade-off. Designing such a system
is challenging due to (a) the huge amount of data to process, and, (b) the need
for fast and accurate estimates. In this paper, we propose a distributed fault
tolerant system that can generate such estimates fast with good accuracy. The
main idea is to keep a small representative sample in memory across multiple
machines and formulate the forecasting problem as queries against the sample.
The key challenge is to find the best strata across the past data, perform
multivariate stratified sampling while ensuring fuzzy fall-back to cover the
small minorities. Our results show a significant improvement over the uniform
and simple stratified sampling strategies which are currently widely used in
the industry
Influence of augmented humans in online interactions during voting events
The advent of the digital era provided a fertile ground for the development
of virtual societies, complex systems influencing real-world dynamics.
Understanding online human behavior and its relevance beyond the digital
boundaries is still an open challenge. Here we show that online social
interactions during a massive voting event can be used to build an accurate map
of real-world political parties and electoral ranks. We provide evidence that
information flow and collective attention are often driven by a special class
of highly influential users, that we name "augmented humans", who exploit
thousands of automated agents, also known as bots, for enhancing their online
influence. We show that augmented humans generate deep information cascades, to
the same extent of news media and other broadcasters, while they uniformly
infiltrate across the full range of identified groups. Digital augmentation
represents the cyber-physical counterpart of the human desire to acquire power
within social systems.Comment: 11 page
ExplaiNE: An Approach for Explaining Network Embedding-based Link Predictions
Networks are powerful data structures, but are challenging to work with for
conventional machine learning methods. Network Embedding (NE) methods attempt
to resolve this by learning vector representations for the nodes, for
subsequent use in downstream machine learning tasks.
Link Prediction (LP) is one such downstream machine learning task that is an
important use case and popular benchmark for NE methods. Unfortunately, while
NE methods perform exceedingly well at this task, they are lacking in
transparency as compared to simpler LP approaches.
We introduce ExplaiNE, an approach to offer counterfactual explanations for
NE-based LP methods, by identifying existing links in the network that explain
the predicted links. ExplaiNE is applicable to a broad class of NE algorithms.
An extensive empirical evaluation for the NE method `Conditional Network
Embedding' in particular demonstrates its accuracy and scalability
Detection of Offensive YouTube Comments, a Performance Comparison of Deep Learning Approaches
Social media data is open, free and available in massive quantities. However, there is a significant limitation in making sense of this data because of its high volume, variety, uncertain veracity, velocity, value and variability. This work provides a comprehensive framework of text processing and analysis performed on YouTube comments having offensive and non-offensive contents.
YouTube is a platform where every age group of people logs in and finds the type of content that most appeals to them. Apart from this, a massive increase in the use of offensive language has been apparent. As there are massive volume of new comments, each comment cannot be removed manually or it will be bad for business for youtubers if they make their comment section unavailable as they will not be able to get any feedback of any kind
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