GUapp is a platform for job-postings search and recommendation for the Italian public administration. The platform offers recommendation services with the aim of matching user skills and requests with job positions available in a given period of time. The recommender system implemented in GUapp, based on Latent Dirichlet Allocation, computes the k-nearest neighbors job positions most similar to the user profile. Furthermore, in order to improve the user experience, GUapp implements a chatbot whose goal is to allow users to interact with the app through natural language. Thanks to that, the search and recommendation process becomes incremental and the user can add new requirements at each stage of the interaction. In this paper we present GUapp, its recommender system, and the chatbot developed for achieving an effective interaction with the user. In the next future, we will carry out in-vivo and in-vitro experiments for evaluating the system and its components in deep.

GUapp: A Conversational Agent for Job Recommendation for the Italian Public Administration

Vito Bellini;Giovanni Maria Biancofiore;Tommaso Di Noia;Eugenio Di Sciascio;Fedelucio Narducci;Claudio Pomo
2020-01-01

Abstract

GUapp is a platform for job-postings search and recommendation for the Italian public administration. The platform offers recommendation services with the aim of matching user skills and requests with job positions available in a given period of time. The recommender system implemented in GUapp, based on Latent Dirichlet Allocation, computes the k-nearest neighbors job positions most similar to the user profile. Furthermore, in order to improve the user experience, GUapp implements a chatbot whose goal is to allow users to interact with the app through natural language. Thanks to that, the search and recommendation process becomes incremental and the user can add new requirements at each stage of the interaction. In this paper we present GUapp, its recommender system, and the chatbot developed for achieving an effective interaction with the user. In the next future, we will carry out in-vivo and in-vitro experiments for evaluating the system and its components in deep.
2020
12th IEEE International Conference on Evolving and Adaptive Intelligent Systems, EAIS 2020
978-1-7281-4384-2
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11589/203380
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