1. User Modeling and Recommender Systems: Introduction to recommender systems Adolfo Ruiz Calleja 06/09/2014

2. Index 2 What is a recommender system? Classification of recommender systems Introduction to the main paradigms of recommender systems Example: Amazon

3. Index 3 What is a recommender system? – Approacher to avoid information overload – Definition of Recommender Systems – Some examples – Added value of the Recommender Systems Classification of recommender systems Introduction to the main paradigms of recommender systems Example: Amazon

4. Approaches to avoid information overload 4 Information retrieval (IR) – Static content + dynamic query – The content is modelled – Example: a library search system Information filtering (IF) – Static query + dynamic content – The query is modelled – Example: anti-spam filter

5. Definition of Recommender Systems 5 Recommender Systems (RS) are information filtering systems that seek to predict the preference that a user would give to an item USERITEM Algorithm rating Set of user attributes

6. Some Examples 6

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10. Added value of the Recommender Systems 10 Provision of personalized recommendations – But it requires that the maintain a user profile Allows to persuade each customer with personalized information Serendipitous discovery Enables to deal with the long tail – Which is very important in the Web

11. Added value of the Recommender Systems 11

12. Index 12 What is a recommender system? Classification of recommender systems – Different classifications – Domain of the recommendation – Purpose of the recommendation – Context of the recommendation – Data collected – Recommendation algorithm Introduction to the main paradigms of recommender systems Example: Amazon

13. Different classifications 13 Domain of the recommender system Purpose of the recommendation Context of the recommendation Data collected Recommendation algorithms Others Privacy Interfaces Software architecture

14. Domain of the recommendation: What is being recommended? 14 Many different examples – Text documents (web pages, news…) – Media (music, movies…) – Products (or product bundles) – Vendors – People – Sequences Huge impact on the recommendation algorithm – Should it recommend twice the same item? – How important is time?

15. Purpose of the recommendation 15 The recommendation itself – E.g. sale a product Education of the users – E.g. track user behavior to provide recommendations Build a community around a particular product – E.g. booking

16. Context of the recommendation: What is the user doing? 16 Can the user be interrupted? – E.g. listening to music vs. shopping Is the user alone or within a group? – E.g. recommend items to users vs. to groups

17. Data collected 17 How are the recommended items described? How are they collected? Whose opinion does the algorithm collect? How is this opinions collected? How are the profiles created? – Explicit / Implicit What kind of personal information is collected? – It opens several ethical issues

18. Recommendation algorithm 18 Which information is taken into account to make the recommendation? How honest is the recommendation? – Business rules may affect – External manipulation Transparency of the algorithm

19. Index 19 What is a recommender system? Classification of recommender systems Introduction to the main paradigms of recommender systems – Idea – Not personalized – Content-based recommendation – Knowledge-based recommendation – Collaborative recommendation Example: Amazon

20. Idea 20 USERITEM Algorithm rating Set of user attributes

21. Not personalized 21 Based on External Community Data Very little information from the user (if any) Simple algorithms They forget about the long tails Example: Tripadvisor or Billboard

22. Content-based recommendation 22 User model is built analyzing user preferences and item attributes Very little information from the user (if any) Do not need to count with a large group of users It is hard for them to deal with subjective characteristics of items Hard to found massively used examples – Personalized news feeds

23. Knowledge-based recommendation 23 Subclass of content-based recommender systems Need explicit information “from the outside” – Included by the user (constraint-based) – Knowledge from experts in the domain (cased-based) Can deal with time spans Can deal with visitors that only appear once House, car or technology recommendation – Realtor

24. Collaborative recommendation 24 Item model is a set of ratings User model is a set of ratings Many different techniques to match the ratings What to do with new things/people/systems? Predominant paradigm

25. Index 25 What is a recommender system? Classification of recommender systems Introduction to the main paradigms of recommender systems Example: Amazon