
AI in Finance: Challenges, Techniques and Opportunities
AI in finance broadly refers to the applications of AI techniques in fin...
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Recurrent Coupled Topic Modeling over Sequential Documents
The abundant sequential documents such as online archival, social media ...
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Graph Learning based Recommender Systems: A Review
Recent years have witnessed the fast development of the emerging topic o...
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COVID19 Modeling: A Review
To tackle the COVID19 pandemic, massive efforts have been made in model...
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Homophily Outlier Detection in NonIID Categorical Data
Most of existing outlier detection methods assume that the outlier facto...
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Deep Reinforcement Learning for Unknown Anomaly Detection
We address a critical yet largely unsolved anomaly detection problem, in...
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Scientific Article Recommendation: Exploiting Common Author Relations and Historical Preferences
Scientific article recommender systems are playing an increasingly impor...
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Unsupervised Heterogeneous Coupling Learning for Categorical Representation
Complex categorical data is often hierarchically coupled with heterogene...
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AI in FinTech: A Research Agenda
Smart FinTech has emerged as a new area that synthesizes and transforms ...
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Deep Learning for Anomaly Detection: A Review
Anomaly detection, a.k.a. outlier detection, has been a lasting yet acti...
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NonIID Recommender Systems: A Review and Framework of Recommendation Paradigm Shifting
While recommendation plays an increasingly critical role in our living, ...
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Coupling Learning of Complex Interactions
Complex applications such as big data analytics involve different forms ...
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Data Science: A Comprehensive Overview
The twentyfirst century has ushered in the age of big data and data eco...
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Data Science: Nature and Pitfalls
Data science is creating very exciting trends as well as significant con...
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Data Science: Challenges and Directions
While data science has emerged as a contentious new scientific field, en...
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Jointly Modeling Intra and Intertransaction Dependencies with Hierarchical Attentive Transaction Embeddings for Nextitem Recommendation
A transactionbased recommender system (TBRS) aims to predict the next i...
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Graph Learning Approaches to Recommender Systems: A Review
Recent years have witnessed the fast development of the emerging topic o...
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Sequential Recommender Systems: Challenges, Progress and Prospects
The emerging topic of sequential recommender systems has attracted incre...
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FiDiRL: Incorporating Deep Reinforcement Learning with FiniteDifference Policy Search for Efficient Learning of Continuous Control
In recent years significant progress has been made in dealing with chall...
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Multiview Informationtheoretic Coclustering for Cooccurrence Data
Multiview clustering has received much attention recently. Most of the ...
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TBQ(σ): Improving Efficiency of Trace Utilization for OffPolicy Reinforcement Learning
Offpolicy reinforcement learning with eligibility traces is challenging...
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A Survey on Sessionbased Recommender Systems
Sessionbased recommender systems (SBRS) are an emerging topic in the re...
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Learning Representations of Ultrahighdimensional Data for Random Distancebased Outlier Detection
Learning expressive lowdimensional representations of ultrahighdimensi...
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Learning Hidden Structures with Relational Models by Adequately Involving Rich Information in A Network
Effectively modelling hidden structures in a network is very practical b...
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Nonparametric Powerlaw Data Clustering
It has always been a great challenge for clustering algorithms to automa...
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A Convergence Theorem for the Graph Shifttype Algorithms
Graph Shift (GS) algorithms are recently focused as a promising approach...
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Dynamic Infinite MixedMembership Stochastic Blockmodel
Directional and pairwise measurements are often used to model interrela...
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Copula MixedMembership Stochastic Blockmodel for IntraSubgroup Correlations
The MixedMembership Stochastic Blockmodel (MMSB) is a popular framework...
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Characterizing A Database of Sequential Behaviors with Latent Dirichlet Hidden Markov Models
This paper proposes a generative model, the latent Dirichlet hidden Mark...
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Longbing Cao
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