Abstract
Typically, career recommendation systems use content-based and collaborative filtering techniques to create a personalized list of vacancies that a candidate might be interested in. These techniques individually have both advantages and disadvantages. Content-based filtering has a high classification accuracy since it is based on data from the user’s resume. Collaborative filtering gives the best predictable result, but it is necessary to collect data on the user’s interests for such a model to work correctly.
This study explores the applicability of hybrid filtering to improve the quality of recommendations and the possible solution to the cold start problem. The cold start problem occurs when the system is unable to form any relation between users and items for which it has insufficient data [1]. While hybrid filtering may help solve the problem of information overload, we also considered the use of skillset vectors to tackle the issue of data sparseness that has also plagued recommender systems. We analyze the applicability and quality of the DistilBERT and BERT models for use in a career recommender system.
A comparative analysis of the results obtained from the online evaluation (user testing) and offline evaluation of the ML model quality is given.
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Shestakova, A., Corradini, A. (2022). Exploring the Use of Machine Learning for Resume Recommendations. In: Prasanna, S.R.M., Karpov, A., Samudravijaya, K., Agrawal, S.S. (eds) Speech and Computer. SPECOM 2022. Lecture Notes in Computer Science(), vol 13721. Springer, Cham. https://doi.org/10.1007/978-3-031-20980-2_53
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