Temporal Learning and Sequence Modeling for a Job Recommender System

TL;DR

This paper introduces a novel job recommendation system that combines temporal learning and sequence modeling, specifically using time-based ranking and RNNs, to better capture user behavior and improve recommendation accuracy.

Contribution

It presents a hybrid approach integrating temporal ranking models and RNN-based sequence modeling for enhanced job recommendations, a new direction in recommender system research.

Findings

Achieved 5th place in RecSys Challenge 2016.

RNN-based model demonstrated strong performance.

Hybrid models improved recommendation quality.

Abstract

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.