Advances in Knowledge Discovery and Data Mining
23rd Pacific-Asia Conference, PAKDD 2019, Macau, China, April 14-17, 2019, Proceedings, Part I
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35 Result(s)
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Computer Science
Computer Appl. in Social and Behavioral Sciences
Information Systems Applications (incl. Internet)
Systems and Data Security
**qi Zhou
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Chapter and Conference Paper
A System for Risk Assessment of Privacy Disclosure
The wide spread and sharing of considerable information promotes the development of many industries such as the health care and so on. However, data owners should pay attention to the problem of privacy preser...
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Chapter and Conference Paper
Adaptive Loading Plan Decision Based upon Limited Transport Capacity
Cargo distribution is one of most critical issues for steel logistics industry, whose core task is to determine cargo loading plan for each truck. Due to cargos far outnumber available transport capacity in st...
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Chapter and Conference Paper
An Efficient Secondary Index for Spatial Data Based on LevelDB
Spatial data has the characteristics of spatial location, unstructured, spatial relationships, massive data. However, the general commercial database itself is difficult to meet the requirements, it’s non-triv...
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Chapter and Conference Paper
Path Query Processing Using Typical Snapshots in Dynamic Road Networks
The shortest path query in road network is a fundamental operation in navigation and location-based services. The existing shortest path algorithms aim at improving efficiency in the static/time-dependent envi...
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Chapter and Conference Paper
Decentralized Embedding Framework for Large-Scale Networks
Network embedding aims to learn vector representations of vertices, that preserve both network structures and properties. However, most existing embedding methods fail to scale to large networks. A few framewo...
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Chapter and Conference Paper
Finding Attribute Diversified Communities in Complex Networks
Recently, finding communities by considering both structure cohesiveness and attribute cohesiveness has begun to generate considerable interest. However, existing works only consider attribute cohesiveness fro...
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Chapter and Conference Paper
DDFL: A Deep Dual Function Learning-Based Model for Recommender Systems
Over the last two decades, latent-based collaborative filtering (CF) has been extensively studied in recommender systems to match users with appropriate items. In general, CF can be categorized into two types:...
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Chapter and Conference Paper
Adversarial Ranking Attack and Defense
Deep Neural Network (DNN) classifiers are vulnerable to adversarial attack, where an imperceptible perturbation could result in misclassification. However, the vulnerability of DNN-based image ranking systems ...
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Chapter and Conference Paper
MetaDistiller: Network Self-Boosting via Meta-Learned Top-Down Distillation
Knowledge Distillation (KD) has been one of the most popular methods to learn a compact model. However, it still suffers from high demand in time and computational resources caused by sequential training pipel...
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Book and Conference Proceedings
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Book and Conference Proceedings
Advances in Knowledge Discovery and Data Mining
23rd Pacific-Asia Conference, PAKDD 2019, Macau, China, April 14-17, 2019, Proceedings, Part III
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Book and Conference Proceedings
Advances in Knowledge Discovery and Data Mining
23rd Pacific-Asia Conference, PAKDD 2019, Macau, China, April 14-17, 2019, Proceedings, Part II
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Chapter and Conference Paper
Multitask Learning for Sparse Failure Prediction
Sparsity is a problem which occurs inherently in many real-world datasets. Sparsity induces an imbalance in data, which has an adverse effect on machine learning and hence reducing the predictability. Previous...
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Chapter and Conference Paper
NEAR: Normalized Network Embedding with Autoencoder for Top-K Item Recommendation
The recommendation system is an important tool both for business and individual users, aiming to generate a personalized recommended list for each user. Many studies have been devoted to improving the accuracy...
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Chapter and Conference Paper
Towards One Reusable Model for Various Software Defect Mining Tasks
Software defect mining is playing an important role in software quality assurance. Many deep neural network based models have been proposed for software defect mining tasks, and have pushed forward the state-o...
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Chapter and Conference Paper
How Much Can A Retailer Sell? Sales Forecasting on Tmall
Time-series forecasting is an important task in both academic and industry, which can be applied to solve many real forecasting problems like stock, water-supply, and sales predictions. In this paper, we study...
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Chapter and Conference Paper
Spatial-Temporal Multi-Task Learning for Within-Field Cotton Yield Prediction
Understanding and accurately predicting within-field spatial variability of crop yield play a key role in site-specific management of crop inputs such as irrigation water and fertilizer for optimized crop prod...
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Chapter and Conference Paper
Consensus Graph Learning for Incomplete Multi-view Clustering
Multi-view data clustering is a fundamental task in current machine learning, known as multi-view clustering. Existing multi-view clustering methods mostly assume that each data instance is sampled in all view...
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Chapter and Conference Paper
Enhancing Collaborative Filtering with Multi-label Classification
This paper presents a multi-label classification based CF framework, MLCF, which improves the quality of recommendation in the presence of data sparsity by learning over a heterogeneous information network co...
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Chapter and Conference Paper
RNE: A Scalable Network Embedding for Billion-Scale Recommendation
Nowadays designing a real recommendation system has been a critical problem for both academic and industry. However, due to the huge number of users and items, the diversity and dynamic property of the user in...
