49 research outputs found
Exploring the Confounding Factors of Academic Career Success: An Empirical Study with Deep Predictive Modeling
Understanding determinants of success in academic careers is critically
important to both scholars and their employing organizations. While
considerable research efforts have been made in this direction, there is still
a lack of a quantitative approach to modeling the academic careers of scholars
due to the massive confounding factors. To this end, in this paper, we propose
to explore the determinants of academic career success through an empirical and
predictive modeling perspective, with a focus on two typical academic honors,
i.e., IEEE Fellow and ACM Fellow. We analyze the importance of different
factors quantitatively, and obtain some insightful findings. Specifically, we
analyze the co-author network and find that potential scholars work closely
with influential scholars early on and more closely as they grow. Then we
compare the academic performance of male and female Fellows. After comparison,
we find that to be elected, females need to put in more effort than males. In
addition, we also find that being a Fellow could not bring the improvements of
citations and productivity growth. We hope these derived factors and findings
can help scholars to improve their competitiveness and develop well in their
academic careers
SetRank: A Setwise Bayesian Approach for Collaborative Ranking from Implicit Feedback
The recent development of online recommender systems has a focus on
collaborative ranking from implicit feedback, such as user clicks and
purchases. Different from explicit ratings, which reflect graded user
preferences, the implicit feedback only generates positive and unobserved
labels. While considerable efforts have been made in this direction, the
well-known pairwise and listwise approaches have still been limited by various
challenges. Specifically, for the pairwise approaches, the assumption of
independent pairwise preference is not always held in practice. Also, the
listwise approaches cannot efficiently accommodate "ties" due to the
precondition of the entire list permutation. To this end, in this paper, we
propose a novel setwise Bayesian approach for collaborative ranking, namely
SetRank, to inherently accommodate the characteristics of implicit feedback in
recommender system. Specifically, SetRank aims at maximizing the posterior
probability of novel setwise preference comparisons and can be implemented with
matrix factorization and neural networks. Meanwhile, we also present the
theoretical analysis of SetRank to show that the bound of excess risk can be
proportional to , where and are the numbers of items and
users, respectively. Finally, extensive experiments on four real-world datasets
clearly validate the superiority of SetRank compared with various
state-of-the-art baselines.Comment: This paper has been accepted in AAAI'2
Joint Air Quality and Weather Prediction Based on Multi-Adversarial Spatiotemporal Networks
Accurate and timely air quality and weather predictions are of great
importance to urban governance and human livelihood. Though many efforts have
been made for air quality or weather prediction, most of them simply employ one
another as feature input, which ignores the inner-connection between two
predictive tasks. On the one hand, the accurate prediction of one task can help
improve another task's performance. On the other hand, geospatially distributed
air quality and weather monitoring stations provide additional hints for
city-wide spatiotemporal dependency modeling. Inspired by the above two
insights, in this paper, we propose the Multi-adversarial spatiotemporal
recurrent Graph Neural Networks (MasterGNN) for joint air quality and weather
predictions. Specifically, we first propose a heterogeneous recurrent graph
neural network to model the spatiotemporal autocorrelation among air quality
and weather monitoring stations. Then, we develop a multi-adversarial graph
learning framework to against observation noise propagation introduced by
spatiotemporal modeling. Moreover, we present an adaptive training strategy by
formulating multi-adversarial learning as a multi-task learning problem.
Finally, extensive experiments on two real-world datasets show that MasterGNN
achieves the best performance compared with seven baselines on both air quality
and weather prediction tasks.Comment: 9 pages, 6 figure
Developing Fairness Rules for Talent Intelligence Management System
Talent management is an important business strategy, but inherently expensive due to the unique, subjective, and developing nature of each talent. Applying artificial intelligence (AI) to analyze large-scale data, talent intelligence management system (TIMS) is intended to address the talent management problems of organizations. While TIMS has greatly improved the efficiency of talent management, especially in the processes of talent selection and matching, high-potential talent discovery and talent turnover prediction, it also brings new challenges. Ethical issues, such as how to maintain fairness when designing and using TIMS, are typical examples. Through the Delphi study in a leading global AI company, this paper proposes eight fairness rules to avoid fairness risks when designing TIMS
Enhancing Person-Job Fit for Talent Recruitment: An Ability-aware Neural Network Approach
The wide spread use of online recruitment services has led to information
explosion in the job market. As a result, the recruiters have to seek the
intelligent ways for Person Job Fit, which is the bridge for adapting the right
job seekers to the right positions. Existing studies on Person Job Fit have a
focus on measuring the matching degree between the talent qualification and the
job requirements mainly based on the manual inspection of human resource
experts despite of the subjective, incomplete, and inefficient nature of the
human judgement. To this end, in this paper, we propose a novel end to end
Ability aware Person Job Fit Neural Network model, which has a goal of reducing
the dependence on manual labour and can provide better interpretation about the
fitting results. The key idea is to exploit the rich information available at
abundant historical job application data. Specifically, we propose a word level
semantic representation for both job requirements and job seekers' experiences
based on Recurrent Neural Network. Along this line, four hierarchical ability
aware attention strategies are designed to measure the different importance of
job requirements for semantic representation, as well as measuring the
different contribution of each job experience to a specific ability
requirement. Finally, extensive experiments on a large scale real world data
set clearly validate the effectiveness and interpretability of the APJFNN
framework compared with several baselines.Comment: This is an extended version of our SIGIR18 pape
The Future of ChatGPT-enabled Labor Market: A Preliminary Study
As a phenomenal large language model, ChatGPT has achieved unparalleled
success in various real-world tasks and increasingly plays an important role in
our daily lives and work. However, extensive concerns are also raised about the
potential ethical issues, especially about whether ChatGPT-like artificial
general intelligence (AGI) will replace human jobs. To this end, in this paper,
we introduce a preliminary data-driven study on the future of ChatGPT-enabled
labor market from the view of Human-AI Symbiosis instead of Human-AI
Confrontation. To be specific, we first conduct an in-depth analysis of
large-scale job posting data in BOSS Zhipin, the largest online recruitment
platform in China. The results indicate that about 28% of occupations in the
current labor market require ChatGPT-related skills. Furthermore, based on a
large-scale occupation-centered knowledge graph, we develop a semantic
information enhanced collaborative filtering algorithm to predict the future
occupation-skill relations in the labor market. As a result, we find that
additional 45% occupations in the future will require ChatGPT-related skills.
In particular, industries related to technology, products, and operations are
expected to have higher proficiency requirements for ChatGPT-related skills,
while the manufacturing, services, education, and health science related
industries will have lower requirements for ChatGPT-related skills
Seq-HGNN: Learning Sequential Node Representation on Heterogeneous Graph
Recent years have witnessed the rapid development of heterogeneous graph
neural networks (HGNNs) in information retrieval (IR) applications. Many
existing HGNNs design a variety of tailor-made graph convolutions to capture
structural and semantic information in heterogeneous graphs. However, existing
HGNNs usually represent each node as a single vector in the multi-layer graph
convolution calculation, which makes the high-level graph convolution layer
fail to distinguish information from different relations and different orders,
resulting in the information loss in the message passing. %insufficient mining
of information. To this end, we propose a novel heterogeneous graph neural
network with sequential node representation, namely Seq-HGNN. To avoid the
information loss caused by the single vector node representation, we first
design a sequential node representation learning mechanism to represent each
node as a sequence of meta-path representations during the node message
passing. Then we propose a heterogeneous representation fusion module,
empowering Seq-HGNN to identify important meta-paths and aggregate their
representations into a compact one. We conduct extensive experiments on four
widely used datasets from Heterogeneous Graph Benchmark (HGB) and Open Graph
Benchmark (OGB). Experimental results show that our proposed method outperforms
state-of-the-art baselines in both accuracy and efficiency. The source code is
available at https://github.com/nobrowning/SEQ_HGNN.Comment: SIGIR 202