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Logo Ateneo
   
Recommender Systems
Professor

 

Prof. Markus Zanker

markus.zanker@aau.at

Alpen-Adria-Universität Klagenfurt

 

Prof. Fabio Stella

stella@disco.unimib.it

University of Milano-Bicocca

Period and location

The lectures will be held in Bicocca U14 building (viale Sarca 336/14)

 16 June 2015 - 2 July 2015

 

 

 

June 16th, 17th, 18th, 22nd, 26th (10:00-13:00): Seminar Room

June  24th (10:00-13:00): T023

June 29th (10:00-12:00, 13:00-15:00): Seminar Room

June 30th, July 01st (10:00-12:00):Seminar Room

July 02nd (10:00-13:00): Seminar Room

 

Registration: Send an email to fabio.stella@disco.unimib.it

 


 

 

 

Topic - Aim organization

Recommender Systems facilitate users' decision making when confronted with complex choice situations by easing the information overload. Technically, Recommender Systems have their roots in different fields such as Information Retrieval, Text Classification, Machine Learning and Decision Support Systems. The course will therefore offer first an introduction to the topic and is open to integrate similar and related topics deriving from PhD projects of participants. The course seeks to provide a comprehensive survey on the state-of-the-art and open research challenges in the recommender system field and therefore includes collaborative and content-based filtering methods (with a particular emphasis on the exploitation of Topic Models) as well as knowledge-based recommendation approaches. Furthermore the course will focus on how to measure and evaluate the efficiency and effectiveness of such systems covering the spectrum of seeing them as technical ranking algorithms and as methods for consumer decision support. Therefore, we will consider research designs from a conceptual perspective and compare them with good assessment practices in other research disciplines like Information Retrieval, Machine Learning or Decision Support Systems. Finally, the course will also cover aspects of making the interaction with personalization mechanisms more transparent to their users and the effects of decision biases in online choice situations. 

 

Program
  1. 1.       Introduction

1.1      Applications and basic concepts

1.2      Recommender systems in participants’ PhD context

1.3      Business models

1.4      Personalization, recommendation and the Filter Bubble discussion

  1. 2.       Collaborative Filtering

2.1      User-based and item-based nearest neighbor recommendation

2.2      Types of ratings

2.3      Further model-based recommendation approaches

  1. 3.       Evaluation of Recommender Systems

3.1      Introduction

3.2      IR evaluation methodology

3.3      ML evaluation methodology

3.4      HCI evaluation methodology

  1. 4.       Topic Models and their application in RS

4.1      Generative models for discrete data

4.2      Probabilistic Topic models

4.3      Software

  1. 5.       Knowledge-based Recommendations

5.1      Knowledge-representation and reasoning

5.2      Conversational recommender systems

5.3      Critiquing-based recommender systems

  1. 6.       Explaining recommendations

6.1      Different explanation approaches

6.2      Study results on the impact of explanations

  1. 7.       Online consumer decision making

7     

7.1    Introduction

7.2    Consumer decision making

7.3    Decision Biases

  1. 8.       Wrap up

8     

8.1    Participants’ feedback in the context of their PhD projects

8.2   Summary and outlook

 

Final Examination

Relazione finale su temi trattati o sul rapporto tra contenuti del corso e propria ricerca

Operating methods

Lezioni frontali + seminari su temi concordati

 

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(C) Copyright 2016 - Dipartimento Informatica Sistemistica e Comunicazione - Viale Sarca, 336
20126 Milano - Edificio U14
redazioneweb@disco.unimib.it - ultimo aggiornamento di questa pagina 21/04/2015