379,018 research outputs found
Knowing your FATE: Friendship, Action and Temporal Explanations for User Engagement Prediction on Social Apps
With the rapid growth and prevalence of social network applications (Apps) in
recent years, understanding user engagement has become increasingly important,
to provide useful insights for future App design and development. While several
promising neural modeling approaches were recently pioneered for accurate user
engagement prediction, their black-box designs are unfortunately limited in
model explainability. In this paper, we study a novel problem of explainable
user engagement prediction for social network Apps. First, we propose a
flexible definition of user engagement for various business scenarios, based on
future metric expectations. Next, we design an end-to-end neural framework,
FATE, which incorporates three key factors that we identify to influence user
engagement, namely friendships, user actions, and temporal dynamics to achieve
explainable engagement predictions. FATE is based on a tensor-based graph
neural network (GNN), LSTM and a mixture attention mechanism, which allows for
(a) predictive explanations based on learned weights across different feature
categories, (b) reduced network complexity, and (c) improved performance in
both prediction accuracy and training/inference time. We conduct extensive
experiments on two large-scale datasets from Snapchat, where FATE outperforms
state-of-the-art approaches by error and
runtime reduction. We also evaluate explanations from FATE, showing strong
quantitative and qualitative performance.Comment: Accepted to KDD 2020 Applied Data Science Trac
DeepInf: Social Influence Prediction with Deep Learning
Social and information networking activities such as on Facebook, Twitter,
WeChat, and Weibo have become an indispensable part of our everyday life, where
we can easily access friends' behaviors and are in turn influenced by them.
Consequently, an effective social influence prediction for each user is
critical for a variety of applications such as online recommendation and
advertising.
Conventional social influence prediction approaches typically design various
hand-crafted rules to extract user- and network-specific features. However,
their effectiveness heavily relies on the knowledge of domain experts. As a
result, it is usually difficult to generalize them into different domains.
Inspired by the recent success of deep neural networks in a wide range of
computing applications, we design an end-to-end framework, DeepInf, to learn
users' latent feature representation for predicting social influence. In
general, DeepInf takes a user's local network as the input to a graph neural
network for learning her latent social representation. We design strategies to
incorporate both network structures and user-specific features into
convolutional neural and attention networks. Extensive experiments on Open
Academic Graph, Twitter, Weibo, and Digg, representing different types of
social and information networks, demonstrate that the proposed end-to-end
model, DeepInf, significantly outperforms traditional feature engineering-based
approaches, suggesting the effectiveness of representation learning for social
applications.Comment: 10 pages, 5 figures, to appear in KDD 2018 proceeding
The Importance Of End-User Analysis In New Information System Adapters: Lessons Learned From Practice
The implementation of Information System (IS) in new-IS adapters can remain unused even when they developed properly. The previous research shows that the unsuccessful IS utilization problem primarily related to behavioral issues rather than technical issues. The behavioral issues should be addressed in the stakeholder analysis, an activity in the inception phase of requirement engineering. End-users of an IS are one of the focuses in stakeholder analysis. We studied the correlation of the end-user analysis in new-IS adapters with the successfulness of IS utilization. We conducted a qualitative studied on 20 IS development projects. The findings show strong correlation of the end-user behavior and the IS adoption. We suggest the end-user analysis is necessary for the IS development project in new-IS adapters. We concluded that it is recommended to formally get the end-user commitment before starting the IS development process.
Keyword : Information System, end-user analysis, new-IS adapters, end-user commitmen
Modeling Paying Behavior in Game Social Networks
Online gaming is one of the largest industries on the Internet, generating tens of billions of dollars in revenues annually. One core problem in online game is to find and convert free users into paying customers, which is of great importance for the sustainable development of almost all online games. Although much research has been conducted, there are still several challenges that remain largely unsolved: What are the fundamental factors that trigger the users to pay? How does users? paying behavior influence each other in the game social network? How to design a prediction model to recognize those potential users who are likely to pay? In this paper, employing two large online games as the basis, we study how a user becomes a new paying user in the games. In particular, we examine how users' paying behavior influences each other in the game social network. We study this problem from various sociological perspectives including strong/weak ties, social structural diversity and social influence. Based on the discovered patterns, we propose a learning framework to predict potential new payers. The framework can learn a model using features associated with users and then use the social relationships between users to refine the learned model. We test the proposed framework using nearly 50 billion user activities from two real games. Our experiments show that the proposed framework significantly improves the prediction accuracy by up to 3-11% compared to several alternative methods. The study also unveils several intriguing social phenomena from the data. For example, influence indeed exists among users for the paying behavior. The likelihood of a user becoming a new paying user is 5 times higher than chance when he has 5 paying neighbors of strong tie. We have deployed the proposed algorithm into the game, and the Lift_Ratio has been improved up to 196% compared to the prior strategy
Exploring the impact of user involvement on health and social care services for cancer in the UK.
This report presents the findings from a study of cancer network partnership groups in the UK. Cancer network partnership groups are regional organisations set up to enable joint working between people affected by cancer and health professionals, with the aim of improving cancer care
Social Network Based Substance Abuse Prevention via Network Modification (A Preliminary Study)
Substance use and abuse is a significant public health problem in the United
States. Group-based intervention programs offer a promising means of preventing
and reducing substance abuse. While effective, unfortunately, inappropriate
intervention groups can result in an increase in deviant behaviors among
participants, a process known as deviancy training. This paper investigates the
problem of optimizing the social influence related to the deviant behavior via
careful construction of the intervention groups. We propose a Mixed Integer
Optimization formulation that decides on the intervention groups, captures the
impact of the groups on the structure of the social network, and models the
impact of these changes on behavior propagation. In addition, we propose a
scalable hybrid meta-heuristic algorithm that combines Mixed Integer
Programming and Large Neighborhood Search to find near-optimal network
partitions. Our algorithm is packaged in the form of GUIDE, an AI-based
decision aid that recommends intervention groups. Being the first quantitative
decision aid of this kind, GUIDE is able to assist practitioners, in particular
social workers, in three key areas: (a) GUIDE proposes near-optimal solutions
that are shown, via extensive simulations, to significantly improve over the
traditional qualitative practices for forming intervention groups; (b) GUIDE is
able to identify circumstances when an intervention will lead to deviancy
training, thus saving time, money, and effort; (c) GUIDE can evaluate current
strategies of group formation and discard strategies that will lead to deviancy
training. In developing GUIDE, we are primarily interested in substance use
interventions among homeless youth as a high risk and vulnerable population.
GUIDE is developed in collaboration with Urban Peak, a homeless-youth serving
organization in Denver, CO, and is under preparation for deployment
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