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Is It Time For a Career Switch?
| Citations: | 5 - 1 self |
BibTeX
@MISC{Wang_isit,
author = {Jian Wang and Yi Zhang and Christian Posse and Anmol Bhasin and Linkedin Corp},
title = {Is It Time For a Career Switch?},
year = {}
}
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Abstract
Tenure is a critical factor for an individual to consider when making a job transition. For instance, software engineers make a job transition to senior software engineers in a span of 2 years on average, or it takes for approximately 3 years for realtors to switch to brokers. While most existing work on recommender systems focuses on finding what to recommend to a user, this paper places emphasis on when to make appropriate recommendations and its impact on the item selection in the context of a job recommender system. The approach we propose, however, is general and can be applied to any recommendation scenario where the decision-making process is dependent on the tenure (i.e., the time interval) between successive decisions. Our approach is inspired by the proportional hazards model in statistics. It models the tenure between two successive decisions and related factors. We further extend the model with a hierarchical Bayesian framework to address the problem of data sparsity. The proposed model estimates the likelihood of a user’s decision to make a job transition at a certain time, which is denoted as the tenure-based decision probability. New and appropriate evaluation metrics are designed to analyze the model’s performance on deciding when is the right time to recommend a job to a user. We validate the soundness of our approach by evaluating it with an anonymous job application dataset across 140+ industries on LinkedIn. Experimental results show that the hierarchical proportional hazards model has better predictability of the user’s decision time, which in turn helps the recommender system to achieve higher utility/user satisfaction.
Keyphrases
job transition career switch successive decision recommender system user decision anonymous job application time interval certain time job recommender system proportional hazard model appropriate recommendation data sparsity appropriate evaluation metric right time utility user satisfaction recommendation scenario model performance tenure-based decision probability related factor hierarchical bayesian framework senior software engineer critical factor user decision time item selection decision-making process hierarchical proportional hazard model experimental result software engineer
