Ogni modulo equivale a 3 crediti ECTS. È possibile scegliere un totale di 10 moduli/30 ECTS nelle seguenti categorie:
- 12-15 crediti ECTS in moduli tecnico-scientifici (TSM)
I moduli TSM trasmettono competenze tecniche specifiche del profilo e si integrano ai moduli di approfondimento decentralizzati. - 9-12 crediti ECTS in basi teoriche ampliate (FTP)
I moduli FTP trattano principalmente basi teoriche come la matematica, la fisica, la teoria dell’informazione, la chimica ecc. I moduli ampliano la competenza scientifica dello studente e contribuiscono a creare un importante sinergia tra i concetti astratti e l’applicazione fondamentale per l’innovazione - 6-9 crediti ECTS in moduli di contesto (CM)
I moduli CM trasmettono competenze supplementari in settori quali gestione delle tecnologie, economia aziendale, comunicazione, gestione dei progetti, diritto dei brevetti, diritto contrattuale ecc.
La descrizione del modulo (scarica il pdf) riporta le informazioni linguistiche per ogni modulo, suddivise nelle seguenti categorie:
- Insegnamento
- Documentazione
- Esame
The module will address the theoretical aspects behind the realisation of Recommendation Systems and will allow students to practice over different use case scenarios. In particular it will address the following RecSys approaches:
- Traditional and Machine Learning based recommendation
- Deep Learning based recommendation
- Complex Networks based recommendation.
Requisiti
- Machine Learning and Data Mining
- Python programming
- Basic calculus, linear algebra and statistics concepts
Obiettivi di apprendimento
Students understand the theoretical aspects behind the realization of Recommendation Systems and they will learn how to build them over different use case scenarios.
They will learn how recommendation systems work, focusing on three different approaches:
- Traditional and Machine Learning based recommendation
- Deep Learning based recommendation
- Complex Networks based recommendation.
They will learn how to deal with classical recommendation challenges like imbalanced data set problems, cold-start problems, and long tail problems. Additionally they will learn how to evaluate recommendation systems.
They will learn how to deal with complex networks and how to exploit network extracted information to enhance recommendation solutions.
Finally they will learn how to build multimodal recommendation systems exploiting social networks metrics and dynamics in order to deal with content spread and users engagement.
They know the current research directions within these domains.
They can reuse the material acquired in this course in their own working environment and apply them to solve their specific problems
Contenuti del modulo
The content of the module includes 3 main topics.
Recommendation Systems (70%):
- Traditional and Machine Learning based Recommendation Systems (Collaborative Filtering, Content Based, Knowledge Based, Hybrid)
- Deep Learning based Recommendation Systems
- Evaluation of Recommendation Systems
- Handling challenges in Recommendation Systems (imbalanced data set problems, cold-start problems, long tail problems)
Complex Networks (20%):
- Network Elements (Handling Networks in Code, Density and Sparsity, Subnetworks, Degree, Multilayer and Temporal Networks, Network Representations)
- Network measures (Hubs, Centrality Measures, Centrality Distributions, The Friendship Paradox, Ultra-Small Worlds, Robustness, Core Decomposition, Transitivity, Similarity)
- Network models (Lattice, Random Networks, Small Worlds, Configuration Model, Preferential Attachment, Other Preferential Models)
- Community Detection
- Dynamic models (Ideas, Information, Influence, Epidemic Spreading, Opinion Dynamics, Search)
- Social Media as Networks (es. X/Twitter, Facebook and Reddit)
Multimodal Systems for Recommendation (10%):
- Complex Networks/Social Networks integration
- Use case: Recommendation Systems for Social Networks content spread and users engagement.
Metodologie di insegnamento e apprendimento
The module is delivered in a flipped classroom format, following a problem-based learning approach. During the lessons, the lecturer will present real-world problems, which the class will work on collaboratively to find solutions.
Throughout this process, the lecturer will facilitate problem solving, introducing new concepts and tools as needed to support the students’ learning journey.
Practical activities will complement the theoretical content, allowing students to apply the concepts and methods studied in a hands-on, experiential way.
Bibliografia
Lecture slides, references to internet resources and books
Scarica il descrittivo completo del modulo
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