Temporal Learning and Sequence Modeling for a Job Recommender System
This work addresses job recommendation for users in a specific challenge, showing incremental improvements through hybrid methods.
The authors tackled job recommendation by combining temporal learning and sequence modeling to capture user-item activity patterns, achieving 5th place among over 100 participants in the RecSys Challenge 2016.
We present our solution to the job recommendation task for RecSys Challenge 2016. The main contribution of our work is to combine temporal learning with sequence modeling to capture complex user-item activity patterns to improve job recommendations. First, we propose a time-based ranking model applied to historical observations and a hybrid matrix factorization over time re-weighted interactions. Second, we exploit sequence properties in user-items activities and develop a RNN-based recommendation model. Our solution achieved 5$^{th}$ place in the challenge among more than 100 participants. Notably, the strong performance of our RNN approach shows a promising new direction in employing sequence modeling for recommendation systems.