An Artificial Immune System for job recommendation

Artificial Immune System is a novel computational intelligence technique inspired by immunology has appeared in the recent few years and takes inspiration from the immune system in order to develop new computational mechanisms to solve problems in a broad range of domain areas. This paper presents a problem oriented approach to design an immunizing solution for job recommendation problem. We will describe the immune system metaphors that are relevant to job recommender system. Then, discuss the design issues that should be taken into account such as, the features of the problem to be modeled, the data representation, the affinity measures, and the immune process that should be tailored for the problem. Finally, the corresponding computational model is presented.

[1]  James Allan,et al.  Matching resumes and jobs based on relevance models , 2007, SIGIR.

[2]  Jason Brownlee,et al.  Clonal selection theory and Clonalg: the clonal selection classification algorithm (CSCA) , 2005 .

[3]  Uwe Aickelin Artificial Immune Systems (AIS) - A New Paradigm for Heuristic Decision Making , 2008, ArXiv.

[4]  Evaggelia Pitoura,et al.  Search result diversification , 2010, SGMD.

[5]  A. M. Madni,et al.  Recommender systems in e-commerce , 2014, 2014 World Automation Congress (WAC).

[6]  Yang Fan,et al.  Job recommender systems: A survey , 2012, 2012 7th International Conference on Computer Science & Education (ICCSE).

[7]  Simon M. Garrett,et al.  How Do We Evaluate Artificial Immune Systems? , 2005, Evolutionary Computation.

[8]  Luca Maria Gambardella,et al.  Ant Algorithms for Discrete Optimization , 1999, Artificial Life.

[9]  Leandro Nunes de Castro,et al.  Artificial Immune Systems: Part I-Basic Theory and Applications , 1999 .

[10]  Fernando José Von Zuben,et al.  Learning and optimization using the clonal selection principle , 2002, IEEE Trans. Evol. Comput..

[11]  D. Dasgupta,et al.  Advances in artificial immune systems , 2006, IEEE Computational Intelligence Magazine.

[12]  Marco Dorigo,et al.  Optimization, Learning and Natural Algorithms , 1992 .

[13]  Hongtao Yu,et al.  Reciprocal Recommendation Algorithm for the Field of Recruitment , 2011 .

[14]  Pin-Chang Chen,et al.  A Fuzzy Multiple Criteria Decision Making Model in Employee Recruitment , 2009 .

[15]  Mark S. Fox,et al.  Semantic Matchmaking for Job Recruitment: An Ontology-Based Hybrid Approach , 2009 .

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

[17]  Simon M. Garrett,et al.  Improved Pattern Recognition with Artificial Clonal Selection? , 2003, ICARIS.

[18]  Huan Wang,et al.  A Job Recommender System Based on User Clustering , 2013, J. Comput..

[19]  Karthik Visweswariah,et al.  PROSPECT: a system for screening candidates for recruitment , 2010, CIKM.

[20]  Shaha T. Al-Otaibi,et al.  Job Recommendation Systems for Enhancing E-recruitment Process , 2012 .

[21]  Qi Cheng On the ultimate complexity of factorials , 2004, Theor. Comput. Sci..

[22]  D.H. Lee,et al.  Fighting Information Overflow with Personalized Comprehensive Information Access: A Proactive Job Recommender , 2007, Third International Conference on Autonomic and Autonomous Systems (ICAS'07).

[23]  Peter Brusilovsky,et al.  Adaptive Hypermedia , 2001, User Modeling and User-Adapted Interaction.

[24]  El-Ghazali Talbi,et al.  Metaheuristics - From Design to Implementation , 2009 .

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