Semantics-aware Recommender Systems based on Knowledge Graphs
Cataldo Musto
University of Bari
Politecnico di Milano, this seminar will be held online
November 9th, 2020
1.30 pm
Contatcs:
Elisabetta Di Nitto
Research Line:
Advanced software architectures and methodologies
University of Bari
Politecnico di Milano, this seminar will be held online
November 9th, 2020
1.30 pm
Contatcs:
Elisabetta Di Nitto
Research Line:
Advanced software architectures and methodologies
Abstract
On November 9th, 2020 at 1.30 pm, Cataldo Musto, University of Bari, will hold an online seminar on “Semantics-aware Recommender Systems based on Knowledge Graphs” .
Recommender Systems represent one of the most disruptive technologies that appeared on the scene in the last decade. As proven by several success stories (e.g., Amazon, Netflix, Spotify, just to name a few) the goal of these systems is to provide users with personalized suggestions about items to be consumed, such as music to listen to, movies to watch, and so on. Generally speaking, personalization mechanisms driven by recommendation algorithms can provide users with accurate and effective suggestions. However, there is still room for improvement: as an example, the information encoded in knowledge graphs, such as DBpedia and Wikidata, recently emerged as an effective mean to further improve the predictive accuracy of these algorithms.
Recommender Systems represent one of the most disruptive technologies that appeared on the scene in the last decade. As proven by several success stories (e.g., Amazon, Netflix, Spotify, just to name a few) the goal of these systems is to provide users with personalized suggestions about items to be consumed, such as music to listen to, movies to watch, and so on. Generally speaking, personalization mechanisms driven by recommendation algorithms can provide users with accurate and effective suggestions. However, there is still room for improvement: as an example, the information encoded in knowledge graphs, such as DBpedia and Wikidata, recently emerged as an effective mean to further improve the predictive accuracy of these algorithms.
In this talk, the topic of semantics-aware recommender systems fed by knowledge graphs will be introduced. In particular, we will introduce the recent advances in the area and we will also show how knowledge graphs can be used to generate natural language explanations supporting the suggestions returned by recommendation algorithms.
Virtual room: https://politecnicomilano.webex.com
Meeting number: 121 083 2260
Password: 6p4fCAgRep2
d966fefaae4d44d58f92a1efc7533459
By video: 1210832260@politecnicomilano.webex.com
or calling to: 62.109.219.4 and insert meeting number
By phone:
+44-20-7660-8149 United Kingdom Toll
Access Code: 121 083 2260