The document summarizes information filtering and collaborative filtering. It discusses how information filtering monitors information to push relevant data to users. Collaborative filtering recommends items to users based on their preferences, taking into account preferences of similar users. The document describes content-based filtering and collaborative/social filtering approaches. It provides details on memory-based and model-based collaborative filtering methods, and discusses hybrid methods that combine collaborative and content-based techniques.

1. IR Presentation
Collaborative Filtering
Presented by-
Diksha R. Gupta
Roll no.:- 7

2. Information
Filtering

3. Contents
 Information Filtering
 – Content-based filtering and
Collaborative filtering
 Content-based Filtering
 A content-based filtering model based on
multiple criteria evaluation
 Collaborative Filtering

4. Information Filtering
 Information Filtering is the process of monitoring large
amounts of dynamically generated information and
pushing to a user the subset of information likely to be
of her/his interest (based on her/his information needs).

5. Information Filtering(Cont…)

6. Information Filtering(cont…)

7. Information Filtering: main
categories

8. Recommender Systems
 Systems for recommending items (e.g. books,
movies, CD’s, web pages, newsgroup messages)
to users based on examples of their preferences.
 Many on-line stores provide recommendations
(e.g. Amazon, CDNow).
 Recommenders have been shown to
substantially increase sales at on-line stores.
 There are two basic approaches to
recommending:
◦ Collaborative Filtering (a.k.a. social filtering)
◦ Content-based

9. Content-based Filtering

10. Collaborative Filtering (Social
Filtering)

11. Collaborative Filtering

12. Collaborative Filtering(cont..)

13. Collaborative Filtering

14. Collaborative Filtering(Cont..)
 Methods for collaborative
recommendations can be
 grouped into two general classes:
 – Memory-based (or heuristic-based)
 – Model-based.

15. Collaborative Filtering(Cont..)

16. Collaborative Filtering(Cont..)
 Model-based methods use the collection of
ratings to learn a model, which is then used
to make rating predictions.
 probabilistic models
 Markov decision processes
 based on machine learning techniques

17. Hybrid Methods
 1. implementing collaborative and content-based
methods separately and combining their predictions
 2. incorporating some content-based characteristics
into a collaborative approach
 3. incorporating some collaborative characteristics
into a content-based approach
 4. constructing a general unifying model that
incorporates both content-based and collaborative
characteristics.

18. Rocchio’ illustrated
: centroid of relevant documents

19. Rocchio’ illustrated
does not separate relevant / nonrelevant.

20. Rocchio’ illustrated
centroid of nonrelevant documents.

21. Rocchio’ illustrated
- difference vector

22. Rocchio’ illustrated
Add difference vector to …

23. Rocchio’ illustrated
… to get

24. Rocchio’ illustrated
separates relevant / nonrelevant perfectly.

25. Rocchio’ illustrated
separates relevant / nonrelevant perfectly.

26. Rocchio Formula
ectorfeedback vnegative
ectorfeedback vpositive
vectorprofileoriginalvectorprofile






0 4 0 8 0 0
1 2 4 0 0 1
2 0 1 1 0 4
-1 6 3 7 0 -3
0 4 0 8 0 0
2 4 8 0 0 2
8 0 4 4 0 16
Original profile
Positive Feedback
Negative feedback
0.1
5.0
25.0
(+)
(-)
New profile

27. THANK ‘S