5 research outputs found
Factorization Bandits for Online Influence Maximization
We study the problem of online influence maximization in social networks. In
this problem, a learner aims to identify the set of "best influencers" in a
network by interacting with it, i.e., repeatedly selecting seed nodes and
observing activation feedback in the network. We capitalize on an important
property of the influence maximization problem named network assortativity,
which is ignored by most existing works in online influence maximization. To
realize network assortativity, we factorize the activation probability on the
edges into latent factors on the corresponding nodes, including influence
factor on the giving nodes and susceptibility factor on the receiving nodes. We
propose an upper confidence bound based online learning solution to estimate
the latent factors, and therefore the activation probabilities. Considerable
regret reduction is achieved by our factorization based online influence
maximization algorithm. And extensive empirical evaluations on two real-world
networks showed the effectiveness of our proposed solution.Comment: 11 pages (including SUPPLEMENT
Targeted Advertising on Social Networks Using Online Variational Tensor Regression
This paper is concerned with online targeted advertising on social networks.
The main technical task we address is to estimate the activation probability
for user pairs, which quantifies the influence one user may have on another
towards purchasing decisions. This is a challenging task because one marketing
episode typically involves a multitude of marketing campaigns/strategies of
different products for highly diverse customers. In this paper, we propose what
we believe is the first tensor-based contextual bandit framework for online
targeted advertising. The proposed framework is designed to accommodate any
number of feature vectors in the form of multi-mode tensor, thereby enabling to
capture the heterogeneity that may exist over user preferences, products, and
campaign strategies in a unified manner. To handle inter-dependency of tensor
modes, we introduce an online variational algorithm with a mean-field
approximation. We empirically confirm that the proposed TensorUCB algorithm
achieves a significant improvement in influence maximization tasks over the
benchmarks, which is attributable to its capability of capturing the
user-product heterogeneity.Comment: 18 pages, 7 figure
Online Modeling and Monitoring of Dependent Processes under Resource Constraints
Adaptive monitoring of a large population of dynamic processes is critical
for the timely detection of abnormal events under limited resources in many
healthcare and engineering systems. Examples include the risk-based disease
screening and condition-based process monitoring. However, existing adaptive
monitoring models either ignore the dependency among processes or overlook the
uncertainty in process modeling. To design an optimal monitoring strategy that
accurately monitors the processes with poor health conditions and actively
collects information for uncertainty reduction, a novel online collaborative
learning method is proposed in this study. The proposed method designs a
collaborative learning-based upper confidence bound (CL-UCB) algorithm to
optimally balance the exploitation and exploration of dependent processes under
limited resources. Efficiency of the proposed method is demonstrated through
theoretical analysis, simulation studies and an empirical study of adaptive
cognitive monitoring in Alzheimer's disease