Career planning consists of a series of decisions that will significantly impact one’s life. Howe... more Career planning consists of a series of decisions that will significantly impact one’s life. However, current recommendation systems have serious limitations, including the lack of effective artificial intelligence algorithms for long-term career planning, and the lack of efficient reinforcement learning (RL) methods for dynamic systems. To improve the long-term recommendation, this work proposes an intelligent sequential career planning system featuring a career path rating mechanism and a new RL method coined as the stochastic subsampling reinforcement learning (SSRL) framework. After proving the effectiveness of this new recommendation system theoretically, we evaluate it computationally by gauging it against several benchmarks under different scenarios representing different user preferences in career planning. Numerical results have demonstrated that our system is superior to other benchmarks in locating promising optimal career paths for users in long-term planning. Case studi...
2019 IEEE International Conference on Data Mining (ICDM)
We investigate the stochastic optimization problem and develop a scalable parallel computing algo... more We investigate the stochastic optimization problem and develop a scalable parallel computing algorithm for deep learning tasks. The key of our study involves a reformation of the objective function for the stochastic optimization in neural network models. We propose a novel update rule, named weighted aggregating stochastic gradient decent, after theoretically analyzing the characteristics of the newly formalized objective function. The new rule introduces a weighted aggregation scheme based on the performance of local workers and does not require a center variable. It assesses the relative importance of local workers and accepts them according to their contributions. Our new rule also allows the implementation of both synchronous and asynchronous parallelization and can result in varying convergence rates. For method evaluation, we benchmark our schemes against the mainstream algorithms, including the elastic averaging SGD in training deep neural networks for classification tasks. We conduct extensive experiments on several classic datasets, and the results confirm the strength of our scheme in accelerating the training of deep architecture and scalable parallelization.
Event extraction is an essential task in natural language processing. Although extensively studie... more Event extraction is an essential task in natural language processing. Although extensively studied, existing work shares issues in three aspects, including (1) the limitations of using original syntactic dependency structure, (2) insufficient consideration of the node level and type information in Graph Attention Network (GAT), and (3) insufficient joint exploitation of the node dependency type and part-of-speech (POS) encoding on the graph structure. To address these issues, we propose a novel framework for open event extraction in documents. Specifically, to obtain an enhanced dependency structure with powerful encoding ability, our model is capable of handling an enriched parallel structure with connected ellipsis nodes. Moreover, through a bidirectional dependency parsing graph, it considers the sequence of order structure and associates the ancestor and descendant nodes. Subsequently, we further exploit node information, such as the node level and type, to strengthen the aggreg...
2018 IEEE International Conference on Data Mining (ICDM), 2018
As a vital process to the success of an organization, salary benchmarking aims at identifying the... more As a vital process to the success of an organization, salary benchmarking aims at identifying the right market rate for each job position. Traditional approaches for salary benchmarking heavily rely on the experiences from domain experts and limited market survey data, which have difficulties in handling the dynamic scenarios with the timely benchmarking requirement. To this end, in this paper, we propose a data-driven approach for intelligent salary benchmarking based on large-scale fine-grained online recruitment data. Specifically, we first construct a salary matrix based on the large-scale recruitment data and creatively formalize the salary benchmarking problem as a matrix completion task. Along this line, we develop a Holistic Salary Benchmarking Matrix Factorization (HSBMF) model for predicting the missing salary information in the salary matrix. Indeed, by integrating multiple confounding factors, such as company similarity, job similarity, and spatial-temporal similarity, HSBMF is able to provide a holistic and dynamic view for fine-grained salary benchmarking. Finally, extensive experiments on large-scale real-world data clearly validate the effectiveness of our approach for job salary benchmarking.
2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2016
Many recent studies on finance and social networks discovered that investor's attention is co... more Many recent studies on finance and social networks discovered that investor's attention is correlated to the financial market movement in terms of the price shocks. Following related findings, a significant and challenging problem is to forecast the direction of the market movement based on vast social media activities. Appropriately processing social networks data and developing models to capture investor's attention on stocks would effectively help financial forecasting. In this paper, we propose and then apply a price shocks forecasting framework, which simultaneously takes the influence of social network users and their opinions about stocks into consideration. Specifically, we develop a new method to estimate social attention to stocks by influence modeling and sentiment analysis. Then, we use it in price shocks forecasting, which we formalize as a classification problem. We also consider the effect of historical market information on the market movement. Finally, we evaluate our framework based on a series of tests on the Chinese stock data. Our results show that the newly proposed measurement of social attention effectively improves the forecasting power of our framework.
ACM Transactions on Intelligent Systems and Technology, 2021
Vehicle mobility optimization in urban areas is a long-standing problem in smart city and spatial... more Vehicle mobility optimization in urban areas is a long-standing problem in smart city and spatial data analysis. Given the complex urban scenario and unpredictable social events, our work focuses on developing a mobile sequential recommendation system to maximize the profitability of vehicle service providers (e.g., taxi drivers). In particular, we treat the dynamic route optimization problem as a long-term sequential decision-making task. A reinforcement-learning framework is proposed to tackle this problem, by integrating a self-check mechanism and a deep neural network for customer pick-up point monitoring. To account for unexpected situations (e.g., the COVID-19 outbreak), our method is designed to be capable of handling related environment changes with a self-adaptive parameter determination mechanism. Based on the yellow taxi data in New York City and vicinity before and after the COVID-19 outbreak, we have conducted comprehensive experiments to evaluate the effectiveness of o...
OF THE DISSERTATION ................................................................................ more OF THE DISSERTATION ................................................................................... ii ACKNOWLEDGEMENTS ..................................................................................................... iv TABLE OF CONTENTS.......................................................................................................... vi LIST OF TABLES ..................................................................................................................viii LIST OF FIGURES .................................................................................................................. ix CHAPTER
the performance of a depth-first Frequent Itemset Miming (FIM) algorithm is closely related to th... more the performance of a depth-first Frequent Itemset Miming (FIM) algorithm is closely related to the total number of recursions which can be modeled as O(n), where k is the maximal recursion depth and n is the branching factor. Many existing approaches focus more on improving support counting rather than on decreasing n and k, which may lead to unsatisfactory performance as they grow. In this paper a novel approach, Binary Itemset Support Counting (BISC), is presented to address these two factors. Let the direct support of an itemset I be the number of transactions with the same itemset as I, BISC can derive the supports of all the itemsets in a database by iteratively updating their direct supports, thus eliminating the need for further recursion. BISC converts a database into its binary representation and combines one-stage BISC and two-stage BISC to minimize the cost of support updating and memory consumption by eliminating redundant updating operations. By applying BISC with the b...
ACM Transactions on Asian and Low-Resource Language Information Processing, 2022
Word segmentation is an essential and challenging task in natural language processing, especially... more Word segmentation is an essential and challenging task in natural language processing, especially for the Chinese language due to its high linguistic complexity. Existing methods for Chinese word segmentation, including statistical machine learning methods and neural network methods, usually have good performance in specific knowledge domains. Given the increasing importance of interdisciplinary and cross-domain studies, one of the challenges in cross-domain word segmentation is to handle the out-of-vocabulary (OOV) words. Existing methods show unsatisfactory performance to meet the practical standard. To this end, we propose a document-level context-aware model that can automatically perceive and identify OOV words from different domains. Our method jointly implements a word-based and a character-based model and then processes the results with a newly proposed reconstruction model. We evaluate the new method by designing and conducting comprehensive experiments on two real-world da...
2020 IEEE International Conference on Data Mining (ICDM), 2020
Partial correlation-based connectivity networks can describe the direct connectivity between feat... more Partial correlation-based connectivity networks can describe the direct connectivity between features while avoiding spurious effects, and hence they can be implemented in diagnosing complex dynamic multivariate systems. However, existing studies mainly focus on single systems that are ill-equipped for incremental learning. Moreover, related methods estimate temporal connectivity network by imposing only sparse regularization without integrating pattern priors (e.g., inter-system shared pattern and intra-system intrinsic pattern), which have been proven effective in limiting noise interference. To this end, we develop an adaptive connectivity estimation model that incorporates prior patterns, namely Sparse Adaptive Meta-Learning Connectivity Network (SAMCN). Specifically, our model extends ideas of the gradient-based meta-learning to capture inter-system shared prior information by generating fast adaptive initialization parameters for the connectivity matrix. Then, a sparse variational autoencoder is proposed to generate a weight matrix for sparse regularization penalty in reweighted LASSO, which helps extract intra-system intrinsic patterns (local manifold structure). Experimental results on both synthetic data and real-world datasets demonstrate that our method is capable of adequately capturing the aforementioned pattern priors. Further, experiments from corresponding classification tasks validate the strength of the prior pattern-aware features connectivity network in resulting in better classification performance.
2018 IEEE International Conference on Data Mining (ICDM), 2018
Following the recent advances of artificial intelligence, financial text mining has gained new po... more Following the recent advances of artificial intelligence, financial text mining has gained new potential to benefit theoretical research with practice impacts. An essential research question for financial text mining is how to accurately identify the actual financial opinions (e.g., bullish or bearish) behind words in plain text. Traditional methods mainly consider this task as a text classification problem with solutions based on machine learning algorithms. However, most of them rely heavily on the hand-crafted features extracted from the text. Indeed, a critical issue along this line is that the latent global and local contexts of the financial opinions usually cannot be fully captured. To this end, we propose a context-aware deep embedding network for financial text mining, named CADEN, by jointly encoding the global and local contextual information. Especially, we capture and include an attitude-aware user embedding to enhance the performance of our model. We validate our method with extensive experiments based on a real-world dataset and several state-of-the-art baselines for investor sentiment recognition. Our results show a consistently superior performance of our approach for identifying the financial opinions from texts of different formats.
2019 IEEE International Conference on Data Mining (ICDM), 2019
In this paper, we develop an efficient parallelheuristic method for solving the global optimizati... more In this paper, we develop an efficient parallelheuristic method for solving the global optimization problemassociated with the ridesharing system. Based on the carefullyformalized problem and objective function, we fully utilize theheuristic characteristics of the algorithm for handling the real-lifeconstraints in ridesharing. Following the principles of simulatedannealing, our method is adaptive in handling the matchingand route optimization tasks. We develop an efficient parallelscheme with simulated annealing, named PCSA, for solving theglobal optimization problem for ridesharing. Our algorithm iscapable to efficiently address the potential of ridesharing byexploiting the mobility information of the ride requests. Basedon extensive experiments on large real-world data, we validatethe performance of our parallel heuristic algorithm. Our resultsconfirm the effectiveness and efficiency of the proposed methodand its superiority over all other benchmarks.
ACM Transactions on Management Information Systems, 2018
There has been increasing interest in exploring the impact of human behavior on financial market ... more There has been increasing interest in exploring the impact of human behavior on financial market dynamics. One of the important related questions is whether attention from society can lead to significant stock price movements or even abnormal returns. To answer the question, we develop a new measurement of social attention, named periodic cumulative degree of social attention , by simultaneously considering the individual influence and the information propagation in social networks. Based on the vast social network data, we evaluate the new attention measurement by testing its significance in explaining future abnormal returns. In addition, we test the forecasting ability of social attention for stock price shocks, defined by the cumulative abnormal returns. Our results provide significant evidence to support the intercorrelated relationship between the social attention and future abnormal returns. The outperformance of the new approach in predicting price shocks is also confirmed b...
ACM Transactions on Management Information Systems, 2017
Nowadays, machine trading contributes significantly to activities in the equity market, and forec... more Nowadays, machine trading contributes significantly to activities in the equity market, and forecasting market movement under high-frequency scenario has become an important topic in finance. A key challenge in high-frequency market forecasting is modeling the dependency structure among stocks and business sectors, with their high dimensionality and the requirement of computational efficiency. As a group of powerful models, neural networks (NNs) have been used to capture the complex structure in many studies. However, most existing applications of NNs only focus on forecasting with daily or monthly data, not with minute-level data that usually contains more noises. In this article, we propose a novel double-layer neural (DNN) network for high-frequency forecasting, with links specially designed to capture dependence structures among stock returns within different business sectors. Various important technical indicators are also included at different layers of the DNN framework. Our ...
IEEE Transactions on Knowledge and Data Engineering, 2017
Advances in real-time location systems have enabled us to collect massive amounts of fine-grained... more Advances in real-time location systems have enabled us to collect massive amounts of fine-grained semantically rich location traces, which provide unparalleled opportunities for understanding human...
Career planning consists of a series of decisions that will significantly impact one’s life. Howe... more Career planning consists of a series of decisions that will significantly impact one’s life. However, current recommendation systems have serious limitations, including the lack of effective artificial intelligence algorithms for long-term career planning, and the lack of efficient reinforcement learning (RL) methods for dynamic systems. To improve the long-term recommendation, this work proposes an intelligent sequential career planning system featuring a career path rating mechanism and a new RL method coined as the stochastic subsampling reinforcement learning (SSRL) framework. After proving the effectiveness of this new recommendation system theoretically, we evaluate it computationally by gauging it against several benchmarks under different scenarios representing different user preferences in career planning. Numerical results have demonstrated that our system is superior to other benchmarks in locating promising optimal career paths for users in long-term planning. Case studi...
2019 IEEE International Conference on Data Mining (ICDM)
We investigate the stochastic optimization problem and develop a scalable parallel computing algo... more We investigate the stochastic optimization problem and develop a scalable parallel computing algorithm for deep learning tasks. The key of our study involves a reformation of the objective function for the stochastic optimization in neural network models. We propose a novel update rule, named weighted aggregating stochastic gradient decent, after theoretically analyzing the characteristics of the newly formalized objective function. The new rule introduces a weighted aggregation scheme based on the performance of local workers and does not require a center variable. It assesses the relative importance of local workers and accepts them according to their contributions. Our new rule also allows the implementation of both synchronous and asynchronous parallelization and can result in varying convergence rates. For method evaluation, we benchmark our schemes against the mainstream algorithms, including the elastic averaging SGD in training deep neural networks for classification tasks. We conduct extensive experiments on several classic datasets, and the results confirm the strength of our scheme in accelerating the training of deep architecture and scalable parallelization.
Event extraction is an essential task in natural language processing. Although extensively studie... more Event extraction is an essential task in natural language processing. Although extensively studied, existing work shares issues in three aspects, including (1) the limitations of using original syntactic dependency structure, (2) insufficient consideration of the node level and type information in Graph Attention Network (GAT), and (3) insufficient joint exploitation of the node dependency type and part-of-speech (POS) encoding on the graph structure. To address these issues, we propose a novel framework for open event extraction in documents. Specifically, to obtain an enhanced dependency structure with powerful encoding ability, our model is capable of handling an enriched parallel structure with connected ellipsis nodes. Moreover, through a bidirectional dependency parsing graph, it considers the sequence of order structure and associates the ancestor and descendant nodes. Subsequently, we further exploit node information, such as the node level and type, to strengthen the aggreg...
2018 IEEE International Conference on Data Mining (ICDM), 2018
As a vital process to the success of an organization, salary benchmarking aims at identifying the... more As a vital process to the success of an organization, salary benchmarking aims at identifying the right market rate for each job position. Traditional approaches for salary benchmarking heavily rely on the experiences from domain experts and limited market survey data, which have difficulties in handling the dynamic scenarios with the timely benchmarking requirement. To this end, in this paper, we propose a data-driven approach for intelligent salary benchmarking based on large-scale fine-grained online recruitment data. Specifically, we first construct a salary matrix based on the large-scale recruitment data and creatively formalize the salary benchmarking problem as a matrix completion task. Along this line, we develop a Holistic Salary Benchmarking Matrix Factorization (HSBMF) model for predicting the missing salary information in the salary matrix. Indeed, by integrating multiple confounding factors, such as company similarity, job similarity, and spatial-temporal similarity, HSBMF is able to provide a holistic and dynamic view for fine-grained salary benchmarking. Finally, extensive experiments on large-scale real-world data clearly validate the effectiveness of our approach for job salary benchmarking.
2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2016
Many recent studies on finance and social networks discovered that investor's attention is co... more Many recent studies on finance and social networks discovered that investor's attention is correlated to the financial market movement in terms of the price shocks. Following related findings, a significant and challenging problem is to forecast the direction of the market movement based on vast social media activities. Appropriately processing social networks data and developing models to capture investor's attention on stocks would effectively help financial forecasting. In this paper, we propose and then apply a price shocks forecasting framework, which simultaneously takes the influence of social network users and their opinions about stocks into consideration. Specifically, we develop a new method to estimate social attention to stocks by influence modeling and sentiment analysis. Then, we use it in price shocks forecasting, which we formalize as a classification problem. We also consider the effect of historical market information on the market movement. Finally, we evaluate our framework based on a series of tests on the Chinese stock data. Our results show that the newly proposed measurement of social attention effectively improves the forecasting power of our framework.
ACM Transactions on Intelligent Systems and Technology, 2021
Vehicle mobility optimization in urban areas is a long-standing problem in smart city and spatial... more Vehicle mobility optimization in urban areas is a long-standing problem in smart city and spatial data analysis. Given the complex urban scenario and unpredictable social events, our work focuses on developing a mobile sequential recommendation system to maximize the profitability of vehicle service providers (e.g., taxi drivers). In particular, we treat the dynamic route optimization problem as a long-term sequential decision-making task. A reinforcement-learning framework is proposed to tackle this problem, by integrating a self-check mechanism and a deep neural network for customer pick-up point monitoring. To account for unexpected situations (e.g., the COVID-19 outbreak), our method is designed to be capable of handling related environment changes with a self-adaptive parameter determination mechanism. Based on the yellow taxi data in New York City and vicinity before and after the COVID-19 outbreak, we have conducted comprehensive experiments to evaluate the effectiveness of o...
OF THE DISSERTATION ................................................................................ more OF THE DISSERTATION ................................................................................... ii ACKNOWLEDGEMENTS ..................................................................................................... iv TABLE OF CONTENTS.......................................................................................................... vi LIST OF TABLES ..................................................................................................................viii LIST OF FIGURES .................................................................................................................. ix CHAPTER
the performance of a depth-first Frequent Itemset Miming (FIM) algorithm is closely related to th... more the performance of a depth-first Frequent Itemset Miming (FIM) algorithm is closely related to the total number of recursions which can be modeled as O(n), where k is the maximal recursion depth and n is the branching factor. Many existing approaches focus more on improving support counting rather than on decreasing n and k, which may lead to unsatisfactory performance as they grow. In this paper a novel approach, Binary Itemset Support Counting (BISC), is presented to address these two factors. Let the direct support of an itemset I be the number of transactions with the same itemset as I, BISC can derive the supports of all the itemsets in a database by iteratively updating their direct supports, thus eliminating the need for further recursion. BISC converts a database into its binary representation and combines one-stage BISC and two-stage BISC to minimize the cost of support updating and memory consumption by eliminating redundant updating operations. By applying BISC with the b...
ACM Transactions on Asian and Low-Resource Language Information Processing, 2022
Word segmentation is an essential and challenging task in natural language processing, especially... more Word segmentation is an essential and challenging task in natural language processing, especially for the Chinese language due to its high linguistic complexity. Existing methods for Chinese word segmentation, including statistical machine learning methods and neural network methods, usually have good performance in specific knowledge domains. Given the increasing importance of interdisciplinary and cross-domain studies, one of the challenges in cross-domain word segmentation is to handle the out-of-vocabulary (OOV) words. Existing methods show unsatisfactory performance to meet the practical standard. To this end, we propose a document-level context-aware model that can automatically perceive and identify OOV words from different domains. Our method jointly implements a word-based and a character-based model and then processes the results with a newly proposed reconstruction model. We evaluate the new method by designing and conducting comprehensive experiments on two real-world da...
2020 IEEE International Conference on Data Mining (ICDM), 2020
Partial correlation-based connectivity networks can describe the direct connectivity between feat... more Partial correlation-based connectivity networks can describe the direct connectivity between features while avoiding spurious effects, and hence they can be implemented in diagnosing complex dynamic multivariate systems. However, existing studies mainly focus on single systems that are ill-equipped for incremental learning. Moreover, related methods estimate temporal connectivity network by imposing only sparse regularization without integrating pattern priors (e.g., inter-system shared pattern and intra-system intrinsic pattern), which have been proven effective in limiting noise interference. To this end, we develop an adaptive connectivity estimation model that incorporates prior patterns, namely Sparse Adaptive Meta-Learning Connectivity Network (SAMCN). Specifically, our model extends ideas of the gradient-based meta-learning to capture inter-system shared prior information by generating fast adaptive initialization parameters for the connectivity matrix. Then, a sparse variational autoencoder is proposed to generate a weight matrix for sparse regularization penalty in reweighted LASSO, which helps extract intra-system intrinsic patterns (local manifold structure). Experimental results on both synthetic data and real-world datasets demonstrate that our method is capable of adequately capturing the aforementioned pattern priors. Further, experiments from corresponding classification tasks validate the strength of the prior pattern-aware features connectivity network in resulting in better classification performance.
2018 IEEE International Conference on Data Mining (ICDM), 2018
Following the recent advances of artificial intelligence, financial text mining has gained new po... more Following the recent advances of artificial intelligence, financial text mining has gained new potential to benefit theoretical research with practice impacts. An essential research question for financial text mining is how to accurately identify the actual financial opinions (e.g., bullish or bearish) behind words in plain text. Traditional methods mainly consider this task as a text classification problem with solutions based on machine learning algorithms. However, most of them rely heavily on the hand-crafted features extracted from the text. Indeed, a critical issue along this line is that the latent global and local contexts of the financial opinions usually cannot be fully captured. To this end, we propose a context-aware deep embedding network for financial text mining, named CADEN, by jointly encoding the global and local contextual information. Especially, we capture and include an attitude-aware user embedding to enhance the performance of our model. We validate our method with extensive experiments based on a real-world dataset and several state-of-the-art baselines for investor sentiment recognition. Our results show a consistently superior performance of our approach for identifying the financial opinions from texts of different formats.
2019 IEEE International Conference on Data Mining (ICDM), 2019
In this paper, we develop an efficient parallelheuristic method for solving the global optimizati... more In this paper, we develop an efficient parallelheuristic method for solving the global optimization problemassociated with the ridesharing system. Based on the carefullyformalized problem and objective function, we fully utilize theheuristic characteristics of the algorithm for handling the real-lifeconstraints in ridesharing. Following the principles of simulatedannealing, our method is adaptive in handling the matchingand route optimization tasks. We develop an efficient parallelscheme with simulated annealing, named PCSA, for solving theglobal optimization problem for ridesharing. Our algorithm iscapable to efficiently address the potential of ridesharing byexploiting the mobility information of the ride requests. Basedon extensive experiments on large real-world data, we validatethe performance of our parallel heuristic algorithm. Our resultsconfirm the effectiveness and efficiency of the proposed methodand its superiority over all other benchmarks.
ACM Transactions on Management Information Systems, 2018
There has been increasing interest in exploring the impact of human behavior on financial market ... more There has been increasing interest in exploring the impact of human behavior on financial market dynamics. One of the important related questions is whether attention from society can lead to significant stock price movements or even abnormal returns. To answer the question, we develop a new measurement of social attention, named periodic cumulative degree of social attention , by simultaneously considering the individual influence and the information propagation in social networks. Based on the vast social network data, we evaluate the new attention measurement by testing its significance in explaining future abnormal returns. In addition, we test the forecasting ability of social attention for stock price shocks, defined by the cumulative abnormal returns. Our results provide significant evidence to support the intercorrelated relationship between the social attention and future abnormal returns. The outperformance of the new approach in predicting price shocks is also confirmed b...
ACM Transactions on Management Information Systems, 2017
Nowadays, machine trading contributes significantly to activities in the equity market, and forec... more Nowadays, machine trading contributes significantly to activities in the equity market, and forecasting market movement under high-frequency scenario has become an important topic in finance. A key challenge in high-frequency market forecasting is modeling the dependency structure among stocks and business sectors, with their high dimensionality and the requirement of computational efficiency. As a group of powerful models, neural networks (NNs) have been used to capture the complex structure in many studies. However, most existing applications of NNs only focus on forecasting with daily or monthly data, not with minute-level data that usually contains more noises. In this article, we propose a novel double-layer neural (DNN) network for high-frequency forecasting, with links specially designed to capture dependence structures among stock returns within different business sectors. Various important technical indicators are also included at different layers of the DNN framework. Our ...
IEEE Transactions on Knowledge and Data Engineering, 2017
Advances in real-time location systems have enabled us to collect massive amounts of fine-grained... more Advances in real-time location systems have enabled us to collect massive amounts of fine-grained semantically rich location traces, which provide unparalleled opportunities for understanding human...
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