General description

Recommender systems have become indispensable for several Web sites, such as Amazon, Netflix and Google News, helping users navigate through the abundance of items. In general, recommender systems facilitate the selection of items by users by issuing recommendations for items they might like. Nowadays, there are numerous recommendation approaches, like neighborhood-based approaches and model-based ones, and a lot of work on specific aspects of recommendations, like the cold start problem, the long tail problem and the evaluation of the recommended items in terms of a variety of parameters, like relevance, surprise and serendipity. Also, more recently, recommendations have more broad applications, beyond products, like news recommendations, links (friends) recommendations and more innovative ones like query recommendations, medicine recommendations, and others.

In this course, we will focus on algorithmic approaches for producing recommendations, such as collaborative and content-based filtering, as well as more interactive and knowledge-based approaches. We will also discuss how to measure the effectiveness of recommender systems. Finally, we will cover emerging topics, such as contextual recommendations, recommendations for groups, packages recommendations, and how we can achieve diversity in recommender systems.

Learning outcomes

After completing the course, the student is expected to:
- know the basic concepts and techniques of recommender systems,
- be able to handle contemporary research issues and problems on the topic, and
- be able to perform a comparative assessment of existing works.

Contents

Collaborative Filtering, Content-based Filtering, Knowledge-based Recommendations, Hybrid Strategies, Contextual Recommendations, Recommendations for Groups, Packages Recommendations, Explanations in Recommender Systems, Diversity in Recommender Systems, Interactive Data Exploration

Teaching methods

Lectures, exercises, student presentations in class. Participation in course work.

Teaching language

Evaluation

Belongs to following study modules

2016–2017

Teaching

Archived Teaching Schedule. Please refer to current Teaching Shedule.

TIETS43 Recommender Systems