Hybrid deep collaborative filtering for job recommendation
Weijian Chen, Xingming Zhang, Haoxiang Wang, Hongjie Xu · 2017
In recent years, deep learning has achieved great success in a series of areas, but there is few published work on deep learning for job recommendation. Most researchers focus on the application of traditional algorithms, most of which still use algorithms like collaborative filtering and content-based filtering. In this paper, we study and improve the existing recommender algorithm based on deep learning and apply it to the field of job recommendation, hoping to solve the problems existing in traditional recommender algorithm. We collect the information of the candidate and the job from a human resources business system, then performed pre-processing operations on collected data, such as data cleaning, data transforming and data reduction, and obtain a human resources data warehouse for job recommender algorithm. In addition, we propose Hybrid Deep Collaborative Filtering (HDCF) algorithm based on Collaborative Deep Learning (CDL) algorithm. With the help of the feature extraction ability of deep learning, HDCF overcomes the shortcomings of traditional collaborative filtering algorithms when dealing with sparse data and cold-start items. Experimental results show that HDCF has better recommendation performance than traditional recommender algorithms such as Probabilistic Matrix Factorization (PMF) and Content-Based Filtering (CBF).