TY - JOUR
T1 - Using autoencoders for session-based job recommendations
AU - Lacic, Emanuel
AU - Reiter-Haas, Markus
AU - Kowald, Dominik
AU - Reddy Dareddy, Manoj
AU - Cho, Junghoo
AU - Lex, Elisabeth
PY - 2020/9/1
Y1 - 2020/9/1
N2 - In this work, we address the problem of providing job recommendations in an online session setting, in which we do not have full user histories. We propose a recommendation approach, which uses different autoencoder architectures to encode sessions from the job domain. The inferred latent session representations are then used in a k-nearest neighbor manner to recommend jobs within a session. We evaluate our approach on three datasets, (1) a proprietary dataset we gathered from the Austrian student job portal Studo Jobs, (2) a dataset released by XING after the RecSys 2017 Challenge and (3) anonymized job applications released by CareerBuilder in 2012. Our results show that autoencoders provide relevant job recommendations as well as maintain a high coverage and, at the same time, can outperform state-of-the-art session-based recommendation techniques in terms of system-based and session-based novelty.
AB - In this work, we address the problem of providing job recommendations in an online session setting, in which we do not have full user histories. We propose a recommendation approach, which uses different autoencoder architectures to encode sessions from the job domain. The inferred latent session representations are then used in a k-nearest neighbor manner to recommend jobs within a session. We evaluate our approach on three datasets, (1) a proprietary dataset we gathered from the Austrian student job portal Studo Jobs, (2) a dataset released by XING after the RecSys 2017 Challenge and (3) anonymized job applications released by CareerBuilder in 2012. Our results show that autoencoders provide relevant job recommendations as well as maintain a high coverage and, at the same time, can outperform state-of-the-art session-based recommendation techniques in terms of system-based and session-based novelty.
KW - Accuracy
KW - Autoencoders
KW - Job recommendations
KW - Novelty
KW - Session embeddings
KW - Session-based recommendation
UR - http://www.scopus.com/inward/record.url?scp=85087387130&partnerID=8YFLogxK
U2 - 10.1007/s11257-020-09269-1
DO - 10.1007/s11257-020-09269-1
M3 - Article
AN - SCOPUS:85087387130
SN - 0924-1868
VL - 30
SP - 617
EP - 658
JO - User Modeling and User-Adapted Interaction
JF - User Modeling and User-Adapted Interaction
IS - 4
ER -