Explaining and exploring job recommendations: a user-driven approach for interacting with knowledge-based job recommender systems

The dynamics of the labor market and the tasks with which jobs are being composed are continuously evolving. Job mobility is not evident, and providing effective recommendations in this context has also been found to be particularly challenging. In this paper, we present Labor Market Explorer, an interactive dashboard that enables job seekers to explore the labor market in a personalized way based on their skills and competences. Through a user-centered design process involving job seekers and job mediators, we developed this dashboard to enable job seekers to explore job recommendations and their required competencies, as well as how these competencies map to their profile. Evaluation results indicate the dashboard empowers job seekers to explore, understand, and find relevant vacancies, mostly independent of their background and age.

[1]  Katrien Verbert,et al.  Interactive recommender systems: A survey of the state of the art and future research challenges and opportunities , 2016, Expert Syst. Appl..

[2]  Katrien Verbert,et al.  IntersectionExplorer, a multi-perspective approach for exploring recommendations , 2019, Int. J. Hum. Comput. Stud..

[3]  Wlodek Zadrozny,et al.  Help me find a job: A graph-based approach for job recommendation at scale , 2017, 2017 IEEE International Conference on Big Data (Big Data).

[4]  Tobias Höllerer,et al.  TasteWeights: a visual interactive hybrid recommender system , 2012, RecSys.

[5]  Adi Botea,et al.  Where can my career take me?: harnessing dialogue for interactive career goal recommendations , 2019, IUI.

[6]  Barry Smyth,et al.  Personalised Retrieval for Online Recruitment Services , 2000 .

[7]  Li Chen,et al.  A user-centric evaluation framework for recommender systems , 2011, RecSys '11.

[8]  John Riedl,et al.  Explaining collaborative filtering recommendations , 2000, CSCW '00.

[9]  Tobias Höllerer,et al.  LinkedVis: exploring social and semantic career recommendations , 2013, IUI '13.

[10]  Katrien Verbert,et al.  Agents Vs. Users: Visual Recommendation of Research Talks with Multiple Dimension of Relevance , 2016, TIIS.

[11]  Hanspeter Pfister,et al.  UpSet: Visualization of Intersecting Sets , 2014, IEEE Transactions on Visualization and Computer Graphics.

[12]  Katrien Verbert,et al.  How Do Different Levels of User Control Affect Cognitive Load and Acceptance of Recommendations? , 2017, IntRS@RecSys.

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

[14]  Nashwa Abdelbaki,et al.  Hybrid Information Filtering Engine for Personalized Job Recommender System , 2018, AMLTA.

[15]  Jordi Conesa,et al.  A Life-long Learning Recommender System to Promote Employability , 2017, Int. J. Emerg. Technol. Learn..

[16]  Tobias Höllerer,et al.  SmallWorlds: Visualizing Social Recommendations , 2010, Comput. Graph. Forum.

[17]  Bart Baesens,et al.  Evaluating recommendation and search in the labor market , 2018, Knowl. Based Syst..

[18]  M. Patton.  Qualitative Research & Evaluation Methods: Integrating Theory and Practice , 2014 .

[19]  Robert van Liere,et al.  Overview of interactive visualization , 2009 .

[20]  Folami Alamudun,et al.  RésuMatcher: A personalized résumé-job matching system , 2016, Expert Syst. Appl..

[21]  Daniel A. Keim,et al.  Challenges in Visual Data Analysis , 2006, Tenth International Conference on Information Visualisation (IV'06).

[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]  Denis Parra,et al.  Moodplay: Interactive Mood-based Music Discovery and Recommendation , 2016, UMAP.

[24]  Nava Tintarev,et al.  RecSys'17 Joint Workshop on Interfaces and Human Decision Making for Recommender Systems , 2017, RecSys.

[25]  Nava Tintarev,et al.  Presenting Diversity Aware Recommendations: Making Challenging News Acceptable , 2017 .

[26]  Mahamudul Hasan,et al.  User interaction analysis to recommend suitable jobs in career-oriented social networking sites , 2016, 2016 International Conference on Data and Software Engineering (ICoDSE).

[27]  George A. Tsihrintzis,et al.  A content based approach for recommending personnel for job positions , 2014, IISA 2014, The 5th International Conference on Information, Intelligence, Systems and Applications.

[28]  Ivania Donoso-Guzmán,et al.  An Interactive Relevance Feedback Interface for Evidence-Based Health Care , 2018, IUI.

[29]  Barry Smyth,et al.  PeerChooser: visual interactive recommendation , 2008, CHI.

[30]  Peter Brusilovsky,et al.  Beyond the Ranked List: User-Driven Exploration and Diversification of Social Recommendation , 2018, IUI.

[31]  Siti Z. Z. Abidin,et al.  Incremental filtering visualization of jobstreet Malaysia ICT jobs , 2017 .