TY - GEN
T1 - Cost-effective and interpretable job skill recommendation with deep reinforcement learning
AU - Sun, Ying
AU - Zhuang, Fuzhen
AU - Zhu, Hengshu
AU - He, Qing
AU - Xiong, Hui
N1 - Funding Information: The research work supported by the National Key Research and Development Program of China (Grant No. 2018YFB1004300), the National Natural Science Foundation of China (Grant No. 61836013, U1836206, U1811461 and 61773361), the Project of Youth Innovation Promotion Association CAS (Grant No. 2017146). Publisher Copyright: © 2021 ACM.
PY - 2021/4/19
Y1 - 2021/4/19
N2 - Nowadays, as organizations operate in very fast-paced and competitive environments, workforce has to be agile and adaptable to regularly learning new job skills. However, it is nontrivial for talents to know which skills to develop at each working stage. To this end, in this paper, we aim to develop a cost-effective recommendation system based on deep reinforcement learning, which can provide personalized and interpretable job skill recommendation for each talent. Specifically, we first design an environment to estimate the utilities of skill learning by mining the massive job advertisement data, which includes a skill-matching-based salary estimator and a frequent itemset-based learning difficulty estimator. Based on the environment, we design a Skill Recommendation Deep Q-Network (SRDQN) with multi-task structure to estimate the long-term skill learning utilities. In particular, SRDQN recommends job skills in a personalized and cost-effective manner; that is, the talents will only learn the recommended necessary skills for achieving their career goals. Finally, extensive experiments on a real-world dataset clearly validate the effectiveness and interpretability of our approach.
AB - Nowadays, as organizations operate in very fast-paced and competitive environments, workforce has to be agile and adaptable to regularly learning new job skills. However, it is nontrivial for talents to know which skills to develop at each working stage. To this end, in this paper, we aim to develop a cost-effective recommendation system based on deep reinforcement learning, which can provide personalized and interpretable job skill recommendation for each talent. Specifically, we first design an environment to estimate the utilities of skill learning by mining the massive job advertisement data, which includes a skill-matching-based salary estimator and a frequent itemset-based learning difficulty estimator. Based on the environment, we design a Skill Recommendation Deep Q-Network (SRDQN) with multi-task structure to estimate the long-term skill learning utilities. In particular, SRDQN recommends job skills in a personalized and cost-effective manner; that is, the talents will only learn the recommended necessary skills for achieving their career goals. Finally, extensive experiments on a real-world dataset clearly validate the effectiveness and interpretability of our approach.
KW - Data Mining
KW - Reinforcement Learning
KW - Skill Recommendation
UR - http://www.scopus.com/inward/record.url?scp=85107905714&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85107905714&partnerID=8YFLogxK
U2 - 10.1145/3442381.3449985
DO - 10.1145/3442381.3449985
M3 - Conference contribution
T3 - The Web Conference 2021 - Proceedings of the World Wide Web Conference, WWW 2021
SP - 3827
EP - 3838
BT - The Web Conference 2021 - Proceedings of the World Wide Web Conference, WWW 2021
PB - Association for Computing Machinery, Inc
T2 - 2021 World Wide Web Conference, WWW 2021
Y2 - 19 April 2021 through 23 April 2021
ER -