We speed up the solution of the mobile sequential recommendation (MSR) problem that requires searching optimal routes for empty taxi cabs through mining massive taxi GPS data. We develop new methods that combine parallel computing and the simulated annealing with novel global and local searches. While existing approaches usually involve costly offline algorithms and methodical pruning of the search space, our new methods provide direct real-time search for the optimal route without the offline preprocessing. Our methods significantly reduce computational time for the high dimensional MSR problems from days to seconds based on the real-world data as well as the synthetic ones. We efficiently provide solutions to MSR problems with thousands of pick-up points without offline training, compared to the published record of 25 pick-up points.

As a key decision-making process in compensation and benefits (C&B) in human resource management, job salary benchmarking (JSB) plays an indispensable role in attracting, motivating, and retaining talent. Whereas the existing research mainly focuses on revealing the essential impacts of personal and organizational characteristics and economic factors on labor costs (e.g., C&B), few studies target optimizing JSB from a practical, data-driven perspective. Traditional approaches suffer from issues that result from using small and sparse data as well as from the limitations of linear statistical models in practice. Furthermore, there are also important technical issues that need to be addressed in the small number of machine learning–based JSB approaches, such as “cold start” issues when considering a brand-new type of company or job or model interpretability issues. To this end, we propose to address the JSB problem with data-driven techniques from a fine-grained perspective by modeling large-scale, real-world online recruitment data. Specifically, we develop a nonparametric Dirichlet process–based latent factor model (NDP-JSB) to jointly model the latent representations of both company and job position and then apply the model to predict salaries based on company and position information. Our model strengthens the usage of data-driven approaches in JSB optimization by addressing the aforementioned issues in existing models. For evaluation, extensive experiments are conducted on two large-scale, real-world data sets. Our results validate the effectiveness of the NDP-JSB and demonstrate its strength in providing interpretable salary benchmarking to benefit complex decision-making processes in talent management.Summary of Contribution: This paper bridges the cutting-edge machine learning techniques to their implementation in a practical operation research problem in human resources. We focus on optimizing the salary-matching work to help the companies to seek reasonable salaries for their positions by proposing a data-driven approach to capture hidden patterns from user and company profiles. The contributions of this work reside in both operation research and computing. We (1) formulate the JSB optimization problem and (2) solve it by developing a data-driven method along with an effective algorithm optimization. Moreover, the proposed methodology has strengths in addressing the issues of data sparseness and result interpretability.

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 studies have further revealed that our SSRL career path recommendation system would encourage people to gradually improve their career paths to maximize long-term benefits. Moreover, we have shown that the initial state (i.e., the first job) can have a significant impact, positively or negatively, on one’s career, while in the long-term view, a carefully planned career path following our recommendation system may mitigate the negative impact of a lackluster beginning in one’s career life.