Current students


PEREZ MAURERA FERNANDO BENJAMINCycle: XXXV

Section: Computer Science and Engineering
Advisor: CREMONESI PAOLO
Tutor: AMIGONI FRANCESCO

Major Research topic:
Research of Recommender Systems from an Industrial Point of View

Abstract:
Nowadays, users are accustomed to consuming digital services, such as online movie renting, music streaming, and online shopping. Several businesses have adapted their model to include themselves in the online market. Moreover, consumers have also changed their habits to do most of their tasks in an online-centered fashion. For instance, they do the expenses online, ordering food from home, and receiving recommendations for new music via mobile applications.

Companies have started to offer personalized services on their platforms to grow their user base and captivate existing users. For instance, by recommending users the content they would like based on their information and behavior. Recommender Systems (RS) are software tools that provide personalized recommendations to users based on different factors, such as item descriptions, previous purchases, and more.

Generally speaking, research in Recommender Systems is usually performed with two, sometimes intersected, points of view: 1) from an academic perspective, where contributions are mostly focused on finding novel recommendation techniques and improving the state of the art 2) from an industrial perspective, where other dimensions become important, such as data gathering, scalability, user interface and experience (navigation, interface design, and more), and where more data sources are available containing features not seen in academic data.

This research work is focused on investigating different topics on Recommender Systems from an industrial perspective. The topics covered in this project include, but are not limited to industrial data sources and their features, use of impressions (previous recommendations to users) in Recommender Systems, evaluation techniques and methodologies, and recommendations in constrained scenarios.