Job recommendation algorithm for graduates based on personalized preference

It is challenging for graduates to find a proper job. Unlike those with occupational history, graduates generally are short of work experience and the support from social network, so they have to face hundreds of recruitment companies. The process of applying for a job is time-consuming, especially in preparing and attending tests and interviews. Not knowing which companies are most proper for them, graduates need to devote their energy and time to preparing for each potential recruitment. This job-hunting strategy can easily lead to employment dissatisfaction or failure. Therefore, it is very helpful to recommend a few most suitable jobs to graduates. Collaborative filtering (CF) method is currently the most frequently adopted and effective recommendation algorithm, but it cannot be directly applied to job recommendation for graduates because graduates generally have no historical records on employment. Besides, job recommendation should take into account graduate preferences for jobs, such as enterprise types and company locations, which are crucial to job choices. To address these challenges, we first analyze the pattern of job choices of graduates. Based on this, we propose a personalized preference collaborative filtering recommendation algorithm (P2CF), which can not only recommend jobs for graduates through massive campus records, but also identify graduate personal preferences for jobs. Graduates are first clustered into different groups according to their academic performances and family economic conditions. Then Bayesian personalized ranking (BPR) method is introduced to calculate the scores of graduate groups to jobs. Finally the scores and graduate personalized preferences are combined to recommend a few potential jobs. P2CF is a recommendation algorithm with hierarchical structure, which takes account of both the group records of job choices and the individual preferences for jobs. Experimental results show that P2CF on job recommendation outperforms state-of-the-art CF methods and identifies graduate personalized preference for jobs accurately.

[1]  Shao-Yuan Li,et al.  BayDNN: Friend Recommendation with Bayesian Personalized Ranking Deep Neural Network , 2017, CIKM.

[2]  Dung Tien Nguyen,et al.  Supporting Career Counseling with User Modeling and Job Matching , 2013, Advanced Computational Methods for Knowledge Engineering.

[3]  Mehrbakhsh Nilashi,et al.  A multi-criteria collaborative filtering recommender system for the tourism domain using Expectation Maximization (EM) and PCA-ANFIS , 2015, Electron. Commer. Res. Appl..

[4]  Anna Peterson,et al.  On the Prowl: How to Hunt and Score Your First Job , 2014 .

[5]  Stevan M. Berber,et al.  A General Rate K/N Convolutional Decoder Based on Neural Networks with Stopping Criterion , 2009, Adv. Artif. Intell..

[6]  Rachel T. A. Croson,et al.  Gender Differences in Preferences , 2009 .

[7]  Yehuda Koren,et al.  Matrix Factorization Techniques for Recommender Systems , 2009, Computer.

[8]  Jie Wang,et al.  Temporal Influences-Aware Collaborative Filtering for QoS-Based Service Recommendation , 2017, 2017 IEEE International Conference on Services Computing (SCC).

[9]  Greg Linden,et al.  Amazon . com Recommendations Item-to-Item Collaborative Filtering , 2001 .

[10]  Magdalini Eirinaki,et al.  CaPaR: A Career Path Recommendation Framework , 2017, 2017 IEEE Third International Conference on Big Data Computing Service and Applications (BigDataService).

[11]  Gail E. Kaiser,et al.  Distributed Authoring and Versioning , 1997 .

[12]  Shaha T. Al-Otaibi,et al.  A survey of job recommender systems , 2012 .

[13]  Lars Schmidt-Thieme,et al.  BPR: Bayesian Personalized Ranking from Implicit Feedback , 2009, UAI.

[14]  Hui Xiong,et al.  Discovery of College Students in Financial Hardship , 2015, 2015 IEEE International Conference on Data Mining.

[15]  G. James Blaine,et al.  Continuous Monitoring of Physiologic Variables with a Dedicated Minicomputer , 1975, Computer.

[16]  Taghi M. Khoshgoftaar,et al.  A Survey of Collaborative Filtering Techniques , 2009, Adv. Artif. Intell..

[17]  Rui Liu,et al.  A hierarchical similarity based job recommendation service framework for university students , 2017, Frontiers of Computer Science.

[18]  P. Rousseeuw.  Silhouettes: a graphical aid to the interpretation and validation of cluster analysis , 1987 .

[19]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[20]  Jun Sun,et al.  Advanced forecasting of career choices for college students based on campus big data , 2018, Frontiers of Computer Science.

[21]  Tat-Seng Chua,et al.  Fast Matrix Factorization for Online Recommendation with Implicit Feedback , 2016, SIGIR.

[22]  Cheng Yang,et al.  A Research of Job Recommendation System Based on Collaborative Filtering , 2014, 2014 Seventh International Symposium on Computational Intelligence and Design.

[23]  Jian Zhu,et al.  An improved K-means clustering algorithm , 2010 .

[24]  Lei Yang,et al.  Forecasting Career Choice for College Students Based on Campus Big Data , 2016, APWeb.

[25]  Tajul Rosli Razak,et al.  Career path recommendation system for UiTM Perlis students using fuzzy logic , 2014, 2014 5th International Conference on Intelligent and Advanced Systems (ICIAS).

[26]  George A. Tsihrintzis,et al.  FoDRA — A new content-based job recommendation algorithm for job seeking and recruiting , 2015, 2015 6th International Conference on Information, Intelligence, Systems and Applications (IISA).

[27]  S. S. Ravi,et al.  Agglomerative Hierarchical Clustering with Constraints: Theoretical and Empirical Results , 2005, PKDD.

[28]  Aristides Gionis,et al.  Machine learned job recommendation , 2011, RecSys '11.

[29]  Xiangnan He,et al.  Attentive Collaborative Filtering: Multimedia Recommendation with Item- and Component-Level Attention , 2017, SIGIR.

[30]  Maoguo Gong,et al.  A Multi-objective Framework for Location Recommendation Based on User Preference , 2017, 2017 13th International Conference on Computational Intelligence and Security (CIS).

[31]  Kai Chen,et al.  Collaborative filtering and deep learning based recommendation system for cold start items , 2017, Expert Syst. Appl..