Users of online job search websites interact with ranked lists of job summaries generated in response to queries, hoping to identify one or more jobs of interest. Hence, the quality of job search rankings becomes a primary factor that affects user satisfaction. In this work, we propose methodologies and measures for evaluating the quality of job search rankings from a user modeling perspective. We start by investigating job seekers' behavior when they are interacting with the generated rankings, leveraging job search interaction logs from Seek.com, a well-known Australasian job search website. The output of this investigation will be an accurate model of job seekers that will be incorporated into an effectiveness metric. Recent proposals for job search ranking models used using two types of metrics to evaluate the quality of the ranking generated by the models: (1) offline metrics, such as NDCG@k (k is set to the number of job summaries shown in the first page), Prec@1, or Mean Reciprocal Rank (MRR); and (2) online metrics, such as click-through rate and job application rate [3, 6].
|Name||SIGIR 2019 - Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval|
|Conference||42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2019|
|Period||21/07/19 → 25/07/19|
- Job search
- User model