Recommender systems are algorithms that mimic the psychology and personality of humans, in order to predict their needs and desires. More formally, recommender systems adopt data-mining and machine-learning techniques to help users in finding attractive and useful products. Products can be almost anything: physical items (e.g., smartphones), places (e.g., restaurants), digital content (e.g., movies and music), and many more. Recommender systems produce recommendations based on different inputs: demographic information about users, ratings and comments on products, individual’s or community’s past preferences and choices, social networks, context of use.

During the last years, recommender systems have seen an increasing adoption in various services. The most famous success story of a recommender system is Netflix. The Netflix company launched a competition (www.netflixprize.com) offering a 1 million dollar prize to anyone able to create a better recommendation system than the one adopted by Netflix itself for its video-streaming service.

The technology behind recommender systems has evolved over the past years into a rich collection of tools that enable practitioners and researchers to develop effective recommenders. We will study the most important of those algorithms, including how they work, how to use them, how to evaluate them, and their strengths and weaknesses in practice. The algorithms we will study include content-based filtering, collaborative filtering, dimensionality reduction, hybrid techniques, cros-domain and context aware techniques. The approach will be hands-on, with the evaluation based on a competition similar to the Netflix prize, which will involve implementation and testing of algorithms. We will also explore the design space for recommender systems, including designing recommender and the surrounding social issues such as identity, privacy, and manipulation.

Prerequisites

Students willing to pass with maximum grades should have a basic familiarity with programming and a general background of mathematics.

Prerequisites

Students willing to pass with maximum grades should have a basic familiarity with programming and a general background of mathematics.

Syllabus

1. Introduction to course and to recommender systems

• Introduction to recommender systems
• Course overview
• Taxonomy of recommender systems
• Description of the competition
  
  

2. Non-personalized recommenders

• Understanding ratings, predictions, and recommendations
• Inferring preferences
• Scales and normalization
• Predictions and recommendations
• Non-personalized recommenders (popularity, average rating)
• Global effects
  
  

3. Content-Based Filtering (CBF) 

• Introduction to content-based recommenders
• Similarity between items
• Similarity between users and items: building user profiles
• Attributes and their normalization: TF-IDF 
  
  

4. Evaluation 

• User and provider utility
• Comparative evaluation: Dead data vs. Laboratory vs. Field study
• User-centered metrics and evaluation: relevance, novelty, diversity
• Relevance: error vs. accuracy metrics vs. rank metrics
• Cold Start, New-Item and New-User problems
• Experimental protocols: datasets and the fallacy of hidden data
• More metrics
• On-line evaluation: basic concepts 
  
  

5. Collaborative Filtering (CF) 

• Introduction to User-User Collaborative Filtering
• Variations and enhancements (k-Nearest-Neighbor and others)
• Introduction to Item-Item Collaborative Filtering
• Strengths and weaknesses of Item-Item vs. User-User CF
• Item-Item and Association Rules 
  
  

6. Dimensionality Reduction Recommenders

• Introduction to dimensionality-reduction recommenders: matrix factorization
• Concepts behind Latent Semantic Analysis and Singular Value Decomposition (SVD)
• Training Matrix Factorization: FunkSVD, ALS and others
• Extending Matrix Factorization: SVD++ and AsySVD
• Probabilistic Matrix Factorization
  
  

7. Advanced topics

• Context-aware recommenders
• Hybrid recommenders
• Cross-domain recommenders
• Learning to rank
• Interfaces for recommender systems: elicitation and explanation
  
  

Students can choose between two types of evaluation.

• Programming level:  students with programming and mathematics experience can participate to the recommender competition that will run during the semester. Students can group to form a team. The competition will end at the end of the semester. Maximum mark will be 30 e lode. Students participating to the competition do not need to pass any further exam for their evaluation. We will publish more details on the competition on the course home page.
• Concept level:  students who are not experienced programmers, or who are primarily interested in understanding the concepts and techniques of recommender systems, without learning to program them, can choose to focus on the conceptual and mathematical content, skipping the competition and the associated practical lectures.  Students not participating to the competition will be evaluated with an oral exam. Maximum mark will be 26/30.

Basic concepts

Advanced book