1. User Modeling and Recommender Systems: recommendation algorithms
Adolfo Ruiz Calleja
04/10/2014

2. Index Introduction Non-personalized recommender algorithms
Content-based recommender algorithms
Collaborative recommendation algorithms

3. Index Introduction Non-personalized recommender algorithms
Content-based recommender algorithms
Collaborative recommendation algorithms

4. Introduction: Added value of the Recommender Systems
Provision of personalized recommendations
Allows to persuade each customer with personalized information
Serendipitous discovery
Enables to deal with the long tail

5. Introduction: Recommender system schema
USER
ITEM
Set of user attributes
Algorithm
Set of user attributes
Set of user attributes
Set of user attributes
Set of user attributes
Set of user attributes
Set of user attributes
Set of user attributes
Set of user attributes
Set of item attributes
rating

6. Introduction: Predictions and recommendations
Outputs of recommender systems
Prediction ≈ how much a user would like an item
Numeric scored related to the predicted opinion of the user about a specific item
Recommendations ≈ suggestion of things you may like
It is typically a list of items
Internally has to make some predictions

7. Introduction: Proceed with caution

8. Index Introduction Non-personalized recommender algorithms
Simple mean
Probabilistic method
Content-based recommender algorithms
Collaborative recommendation algorithms

9. Not personalized recommender algorithms
USER
ITEM
Set of user attributes
Algorithm
Set of user attributes
Set of user attributes
Set of user attributes
Set of user attributes
Set of user attributes
Set of user attributes
Set of user attributes
Set of user attributes
Set of item attributes
rating

10. Not personalized recommender algorithms
Based on External Community Data
Can know ephemeral information from the user
Example: Tripadvisor or Booking

11. Simple mean

12. Probability method

13. Not personalized recommender algorithms
 Very simple algorithms
 They forget about the long tail
 When there are lot of raters predictions tend to median score
Self-selection bias
Diversity of raters
 Pretty bad accuracy

14. Index Introduction Non-personalized recommender algorithms
Content-based recommender algorithms
Explicit decision model
The vector space model
Collaborative recommendation algorithms

15. Content-based recommendation
USER
ITEM
Set of user attributes
Algorithm
Set of user attributes
Set of user attributes
Set of user attributes
Set of user attributes
Set of user attributes
Set of user attributes
Set of user attributes
Set of user attributes
Set of item attributes
rating

16. Content-based recommendation
User model is built analyzing user preferences and item attributes
Hard to found massively used examples
Personalized news feeds

17. Explicit decision model

18. Explicit decision model
 Very well known method in many domains
 The decision tree can be automatically built
No need to formalize domain knowledge
 Can be used with small numbers of features
But recommender systems typically need very many
  They are almost never used

19. The vector space model

20. The vector space model
Which factors to consider in the item description?
Possibility to use keyword vector
It can be automatically extracted from text
But not only for textual items!!
We can aggregate keywords
But how?
How to normalize the vector space?
Hard if it is not automatically done
Term Frequency-Inverse Document Frequency
Do we trust on it?

21. The vector space model How to build the user profile?
If I like it, it is important for me
Sometimes something I do not like may be relevant or viceversa
Problem of how to update user profiles
Are new items more important than previous ones?
Short term vs. Long term

22. The vector space model  We do not need lot of users
 Easy to compute and simple to implement
 Flexible
Easy to integrate with other approaches
Quickly adapt to changes
:S Hard to find out the factors and their weights
 Cannot deal with subjective aspects of the items
 Competitor items are frequently retrieved
 Too simplified model
Results are not accurate as with other approaches

23. Index Introduction Non-personalized recommender algorithms
Content-based recommender algorithms
Collaborative recommendation algorithms
User-based nearest neighbor recommendation
Item-based nearest neighbor recommendation

24. Collaborative recommendation algorithms
USER
ITEM
Set of user attributes
Algorithm
Set of user attributes
Set of user attributes
Set of user attributes
Set of user attributes
Set of user attributes
Set of user attributes
Set of user attributes
Set of user attributes
Set of item attributes
rating

25. Collaborative recommendation algorithms
Item model is a set of ratings
User model is a set of ratings
Predominant paradigm

26. User-based nearest neighbor recommendation

27. User-based nearest neighbor recommendation
Pearson correlation coefficient
There are other algorithms
But commonly provide less accurate results
Cosine correlation is becoming on fashion
Pearson correlation has some deficiencies
What if two users have few items in common?
What if the ratings are unary data?
What if something is loved or hated by the whole community?

28. User-based nearest neighbor recommendation
Processing time = O(N^2*M)
But not in real life
Neighborhood selection
20 to 50 neighbors (sometimes up to 100)
Define number of neighbors or a threshold
Better processing time O(N*M)
Less noise
Reduce coverage

29. User-based nearest neighbor recommendation
Precomputed neighborhood
Better response time
Need to be frequently update (it is not a good idea to define clusters)

30. User-based nearest neighbor recommendation
  Very popular
 Based on subjective information
 Very many variants and possible configurations
 What do we do with new items?
 What do we do with new users?
 Need of (similar) users
 Data sparcity is a problem

31. Item-based nearest neighbor recommendation

32. Item-based nearest neighbor recommendation
Pearson correlation coefficient or cosine similarity
But now the neighborhood is formed by items!!
A model should be built
Processing time = O (I^2)
It is always precomputed
Do not need to save all the model
Memory used vs. accuracy and coverage
Items are much more stable that users
But they still need to be updated

33. Item-based nearest neighbor recommendation
 Efficient algorithm
 Scales very well
 Data sparcity is not a big problem
 Creates nice recommendation lists
 We still need to deal with the cold-start
 Memory use

34. User Modeling and Recommender Systems: recommendation algorithms
Adolfo Ruiz Calleja
04/10/2014