Temporal learning and sequence modeling for a job recommender system

Kuan Liu, Xing Shi, Anoop Kumar, Linhong Zhu, Prem Natarajan
2016 Proceedings of the Recommender Systems Challenge on - RecSys Challenge '16  
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
more » ... on 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.
doi:10.1145/2987538.2987540 dblp:conf/recsys/LiuSKZN16 fatcat:wsmeotl5qvbhnixxiog5jexwzu