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Mo 20.01.2020, 16:00 - 17:00 Uhr TU Berlin, EN building, seminar room EN 719 (7th floor), Einsteinufer 17, 10587 Berlin: "Job Recommendations in a Marketplace with Multiple Stakeholders" (Flavio Clesio, MyHammer AG)

The focus of this work is on build a framework of a recommendation and matching algorithm in the context of Online Job marketplace. Considering all characteristics of a marketplace given by Banerjee et. al. (2017), we consider also that Job Recommendations has the following characteristics: (i) explicit intention of a Job Seeker to perform matches or non-optimal matches outside their preference, (ii) for the job Poster have the best pool of candidates to perform the best decision as possible, (iii) the indivisibility of a job, i.e. it’s a zero-sum game where only a single candidate can have the job, (iv) the jobs most of the time has some expiration date where the Job Platform not only needs to bring the best match between job seeker and the job placer, but this match can occur in a timely way for all jobs, (v) job platforms do not have the full track if the job seeker was accepted by the job poster. Said that, the main objective will be to ensure that all jobs placed receives job seekers, and the latest receives the job listings in a finite preference ranked list, (vi) job platforms needs to ensure not only the best matching between the job seekers and job posters but this platform needs to consider also their own interests in terms of economics. Their business model can be based in facilitate liquidity or placing a cost in other stakeholders.

Flavio Clesio is a Machine Learning Engineer and Data Scientist at MyHammer AG in Berlin. He obtained his master’s degree in the field of Applied Computational Intelligence in exotic Credit Derivatives as Non-Performing Loans. His current research focuses on recommendations in Job marketplace with multiple-stakeholders, Natural Processing Language/Text Classification for German language, Computer Vision for German Handwerkskarte and Gewerbeanmeldung recognition and Security and Countermeasures in Machine Learning development. In addition, he worked in several distinct industries like Financial Markets, Revenue Assurance in Telecommunications, analysis and experimentation in user behavior in mobile platforms and Data Pipelining in real time for Food Delivery in a global platform that attended more than 42 countries. Nowadays he’s working in scalable Machine Learning production systems for projects in Job Matching and Recommendation in German market and applied Deep Learning for document recognition. Flavio has taught a number of courses at Universities in some subjects as Big Data Platforms (Cassandra, Spark and Spark MLLib), Data Warehousing and Scalable ETL systems, Multidimensional Data Warehousing modelling and also Strategic Information Management. Some of his recent industry work has been published at top industry conferences including Strata Data in Singapore, Spark Conference in Dublin, (Real world applied ML conference), Redis Summit and several other local meetups for Google Developer Groups, Facebook Developer Circles and in the Data Council chapter in Berlin.

Base References (in order of importance for this research):

Mehrotra, Rishabh. "Recommendations in a Marketplace" in RecSys 2019 tutorials.

Abdollahpouri, Himan, Gediminas Adomavicius, Robin Burke, Ido Guy, Dietmar Jannach, Toshihiro Kamishima, Jan Krasnodebski, and Luiz Pizzato. "Beyond Personalization: Research Directions in Multistakeholder Recommendation." arXiv preprint arXiv:1905.01986 (2019).

Mehrotra, Rishabh, et al. "Towards a fair marketplace: Counterfactual evaluation of the trade-off between relevance, fairness & satisfaction in recommendation systems." In Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 2243-2251. ACM, 2018.

Burke, Robin D., Himan Abdollahpouri, Bamshad Mobasher, and Trinadh Gupta. "Towards Multi-Stakeholder Utility Evaluation of Recommender Systems." In UMAP (Extended Proceedings). 2016.

Banerjee S, Gollapudi S, Kollias K, Munagala K (2017) Segmenting two-sided markets. Proceedings of the 26th International Conference on World Wide Web, 63{72}