1. Recommender Systems
Copyright: Dietmar Jannah, Markus Zanker and Gerhard Friedrich
(slides based on their IJCAI talk „Tutorial: Recommender Systems”)

3. Recommender systems – motivation
Joseph Pine: „Mass Customization” (1993)
The age of standard, universal, mass products is over
Various customers, various demands, heterogenous (personalised) products are needed
Jeff Bezos (Amazon, CEO)
„If I’ve got 2 million customer I have to have 2 million shops on the web”
User adaptation!
slide from: Engedy Balázs: Ajánlórendszerek

4. Recommender systems – motivation
Problem of selection
There are thousands of available products
The customer has to find the best products among them (information overload) 
The possible items have to be filtered/sorted!
Chief objective of recommender systems:
The customer sees only relevent products
Personalised recommendations are needed! 
The opportunity:
The tracking/information harvesting about web shoping is simple
It’s great for the shop keepers as it can indicate additional sales and returning cusotmers 
User adaptation!
slide: Engedy Balázs: Ajánlórendszerek

7. 100K movie, 10M user, 1000M ratings

8. „Long tail”
30% of sales (amazon)

9. Recommendation task U - users, I - items,
f : supervised sample from the U×I → R mapping (rating)
explicit vs implicit (clicks, time spent on reading etc) ratings
Machine learning task:
Find f’: U×I → R which
estimates f and
fully defined in U×I.
Recommender system:

10. Recommender systems - approaches
Collaborative filtering: exclusively the ratings are utilised
user-based: prediction is based on similar users’ preferences
item-based: prediction is based on the ratings of similar items
Content-based recommendation: items (content) are described by a feature set and we use only the target user’s history
Hybrid methods: the combinations of the two main approaches

11. Evaluation Real evaluation metrics: User satisfaction!
Purchase (like) of recommended products
Increment in sales?

12. „Online” evaluation Live system, control group
(Jannach, Hegelich 2009)
Game app download site
150K user 6+1 groups
Page visits as implicit rating
3.6% more download in the groups with recommendations
No considerable difference among recommender systems

13. „Offline” evaluation
Create train and test set from historical data, then use regression metrics:
or learning-to-rank evaluation metrics

14. Collaborative filtering

15. Collaborative filtering
Basic idea:
Users give ratings (explicit or implicit) for items
The users who behaved similarly to me will behave similarly in the future

16. User-based nearest neighbour recommendation
Select to most similar users (peers) to the target user who rated the target item
Prediction is an aggregation (e.g. average) of the ratings of the peers

17. User-based nearest neighbour recommendation
Similarity metric?
Number of peers?
Aggregation method?

18. Similarity metric: correlation
The similarity of two users(’ history)
We consider only the items which are rated by both of them

19. Aggregation Average weighted by the similarity
Normalised for the divergence of the user’s own mean (modelling of pesimistic and optimistic users)

20. Further problems
The items might differnt weight in the similarity metric
The item where everybody agrees on should have lower weight
Variance of ratings per item should be incorporated into the similarity metric
The similarity calculation is based on the mutally rated items. If the number of these items is small it can bias the similarities
The length of the vectors should be taken into account
Recognition of peers:
Fix k or a threshold for the similarity values

21. Item-based nearest neighburhood recommendation
|U| >> |I|

22. The „cold start” problem
If a new user or a new item is introduced we don’t have any ratings. How to find similar objects?
„Force” the users for rating
Recursive collaborative filtering:

23. Dimension reduction-based recommendations

24. Dimension reduction-based recommendations

25. Collaborative filtering
Intuitive
Performs fine in practice
No need for feature engineering
It performs well with a critical mass
Computational challenges because of the huge matrix…
Incorporating external information is difficult

26. Content-based recommendation systems

27. Content-based recommendation systems
Use exclusively the history of the target user
Items are described by features
e.g.: actors, director, category, words in the description
Train a regression model for each of the user based on the content features

28. Content-based recommendation systems

29. Content-based recommendation systems
Independent from other users (no need for critical mass)
Recommendation can be given for a single user
The cold start problem is smaller
No need for storing/handling a huge matrix
It recommends from the long tail
It can give you a „user model”

30. Content-based recommendation systems
Feature engineering is domain-specific and requires external data collection
The filter bubble problem:
The greatest predicted rating might be a wrong recommendation as it „overfits” to the user’s preferences
E.g. if the user rated only Hungarian and Chinese restaurants the system won’t recommend a Greek restaurant (even it’s the best in the town)
A new user has to be modeled, i.e. a sufficient personal training data is needed

31. Hybrid recommender systems
Content-based → collaborative
We can use content-based prediction at users with many training examples and collaboration at others
The prediction of content-based models can be used in recursive collaborative filtering
Collaborative → content-based
Features can be extracted from other users’ ratings

32. Summary Collaborative filtering Content-based filtering
User- and item-based nearest neighbour recommendation
Content-based filtering