1. RECOMMENDATION SYSTEMS
ÖZNUR KIRMEMİŞ

2. OUTLINE INTRODUCTION FORMALIZATION OF THE PROBLEM APPROACHES
COLLABORATIVE
CONTENT BASED
HYBRID
CONCLUSION

3. PAPERS
1. Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions, Gediminas Adomavicius, Alexander Tuzhilin IEEE Transactions on Knowledge and Data Engineering(June 2005)
2. Content-Boosted Collaborative Filtering for Improved Recommendations, Prem Melville, Raymond J. Mooney and Ramadass Nagarajan, Proceedings of the Eighteenth National Conference on Artificial Intelligence(AAAI-2002)
3. Recommendation as Classification: Using Social and Content-Based Information in Recommendation, Chumki Basu, Haym Hirsh,William Cohen(AAAI-1998)

4. PART 1:
INTRODUCTION
ghjgjgjhgggjhgjhgjggjh

5. Recommendations
We are in the Information society. The quantity of new information available every day goes over our limited processing capabilities.
We face far more choices than we can try in the world, like, which book shall I read, which movie is worth watching, where I shall have dinner tonight, etc.
For this reason, we need something able to suggest us only the worthwhile information.
Make search space smaller!
Search
Recommendations
Items
Products, web sites, blogs, news items, …

6. Recommendations
Acting upon recommendations from other people is a normal part of life.
By using recommendations we can take a shortcut to the things we like without having to try many things we dislike or without having to acquire all the knowledge to make an informed decision.
Recommender systems(RS) automate this facility.
Recommendation systems are thus a solution for information overload.

7. DEFINITION OF RS programs which attempt to predict
items (movies, music, books, news,
web pages) that a user may be
interested in, given some information
about the user's profile

8. Recommendation Systems
Based on a synthesis of ideas from;
Artificial Intelligence
Natural Language Processing
Human-Computer Interaction
Sociology
Information Retrieval
and the technology of the WWW

9. GENERIC RS For a typical recommender system, there are three steps:
The user provides some form of input to the system. These inputs can be both explicit and implicit . Ratings submitted by users are among explicit inputs whereas the URLs visited by a user and time spent reading a web site are among possible implicit inputs.
These inputs are brought together to form a representation of the user's likes and dislikes. This representation could be as simple as a matrix of items-ratings, or as complex as a data structure combining both content and rating information.
The system computes recommendations using these user profiles.
Even though the steps are essentially the same for most recommender systems, there have been different approaches to both step 2 and 3.

10. Current Examples MovieLens Movie recommendation
makes use of collaborative filtering technology
gathers user preferences by asking the user to rate movies.
searches for similar profiles (i.e. users that share the same or similar taste) and uses them to generate new suggestions.

11. Current Examples Amazon LIBRA Book recommendations
recommends books frequently purchased by customers who purchased the selected book
customers receive text recommendations based on the opinions of other customers
LIBRA
Combines a content-based approach with machine learning

12. Current Examples
Cinemax.com
Moviecritic: movies again
And much more……

13. FORMALIZATION OF THE PROBLEM
PART 2:
FORMALIZATION
OF THE PROBLEM

14. Formal Model<C,S,u>
Let C be the set of all users or customers and let S be the set of all possible items that can be recommended, such as books, movies, or restaurants.
S = set of Items
C = set of Customers
Let u be a utility function that measures the usefulness of item s to user c
Utility function u:
C × S → R,

15. Utility Function Utility function u: C × S → R, R
e.g., 0-5 stars, real number in [0,1]
u(c1,s1) = r1; u(c1,s2) = r2;.....
Recommendation: for each user c є C, choose such item si є S that maximizes the user’s utility

16. USER SPACE && ITEM SPACE
USER SPACE(C):
can be defined with a profile that includes various user characteristics, such as age, gender, income, marital status, etc.
ITEM SPACE(S):
Similarly, each element of the item space S can be defined with a set of characteristics.
Ex; (in a movie recommendation application):
S: a collection of movies,
each movie can be represented not only by its ID, but also by its title, genre, director, year of release, leading actors, etc.

17. UTILITY FUNCTION
The central problem of recommender systems lies in that utility u is usually not defined on the whole CXS space, but only on some subset of it.
This means u needs to be extrapolated to the whole space CXS.
The recommendation engine should be able to estimate the ratings of the nonrated item/user combinations and issue appropriate recommendations based on these predictions.

18. Example: Utility Matrix
King Kong
Garfield
Matrix
Usual Suspects
Ayşe
Ali
Veli
Hasan
Gathering “known” ratings for matrix
Extrapolate unknown ratings from known ratings

19. EXTRAPOLATION Extrapolations from known ratings are done by
Specifying heuristics that defines the utility function and validating its performance.
Estimating the utility function that optimizes certain performace criterion, such as the mean square error.
Once the unknown ratings are estimated, recommendations to a user are made by selecting the highest rating among all the estimated ratings for that user.
Alternatively, we can recommend the N best items to a user.

20. APPROACHES Content Based Collaborative Hybrid
PART 3:
APPROACHES
Content Based
Collaborative
Hybrid

21. APPROACHES
Recommender systems are usually classified into the following categories, based on how recommendations are made:
Content-based recommendations:
The user will be recommended items similar to the ones the user preferred in the past, similarity between user profile and item profile, or similarity between item profiles.
Collaborative recommendations:
aim to identify users that have relevant interests and preferences by calculating similarities and dissimilarities between user profiles
The user will be recommended items that are preferred by other people with similar tastes and preferences.
Hybrid approaches:
These methods combine collaborative and content-based methods.

22. CONTENT BASED METHODS

23. Content-based Methods
Main idea:
recommend items to customer C similar to previous items rated highly by C
No similar user information!!
Formalization:
the utility u(c,s) of item s for user c is estimated based on
the utilities u(c,si) assigned by user c to items si є S that are “similar” to item s.

24. Content-based Methods
has its roots in information retrieval and information filtering research.
The improvement over the traditional information retrieval approaches comes from the use of user profiles that contain information about users’ tastes, preferences, and needs.
The profiling information
can be obtained from users explicitly, e.g., through questionnaires, or
implicitly—learned from their transactional behavior over time.
Can use a machine learning algorithm to induce a profile of the users preferences

25. Plan of action(Item Profile+User Profile+Prediction Mechanism)
Item profiles
likes
recommend objects with similar content, same color, shape,..
build
recommend
Red
Circles
Triangles
match
User profile

26. Item Profiles For each item, create an item profile
Let Content(s) be an item profile,
a set of attributes characterizing item s.
movies: author, title, actor, director
text: set of “important” words in document
attributes are used to determine the appropriateness of the item for recommendation purposes.

27. Item Profiles How attributes determined? straightforward
By deciding which slots are important
Slots: Author,Title,Editorial Reviews,..etc
By processing texts
The “importance” (or “informativeness”) of word kj in document dj is determined with some weighting measure wij that can be defined in several different ways.
One of the best-known measures for specifying keyword weights in Information Retrieval is the term frequency/inverse document frequency (TF-IDF) measure.

28. User profiles
Let ContentBasedProfile(c) be the profile of user c containing preferences of this user. These profiles are obtained by analyzing the content of the items previously seen and constructed using keyword analysis techniques from information retrieval.
For example, ContentBasedProfile(c) can be defined as a vector of weights (wc1, , wck), where each weight wci denotes the importance of keyword ki to user c and can be computed from individually rated content vectors using a variety of techniques.

29. Prediction
In content-based systems, the utility function u(c,s) is usually defined as:
Especially, recommending Web pages, both ContentBasedProfile(c) of user c and Content(s) of document s can be represented as TF-IDF vectors and of keyword weights.
Moreover, utility function u(c,s) is usually represented in the information retrieval literature by some scoring heuristic defined in terms of vectors mentioned above, such as the cosine similarity measure. K is the total number of keywords in the system.

30. LIBRA Learning Intelligent Book Recommending Agent
Content-based recommender for books using information about titles extracted from Amazon.
Uses information extraction from the web to organize text into fields:
Author
Title
Editorial Reviews
Customer Comments
Subject terms
Related authors
Related titles

31. EXAMPLE: LIBRA System Amazon Pages LIBRA Database Information
Extraction
Machine Learning
Learner
Predictor
Rated
Examples
User Profile
Recommendations
1.~~~~~~
2.~~~~~~~
3.~~~~~ :

32. Sample Extracted Information
Title: <The Age of Spiritual Machines: When Computers Exceed Human Intelligence>
Author: <Ray Kurzweil>
Price: <11.96>
Publication Date: <January 2000>
ISBN: < >
Related Titles: <Title: <Robot: Mere Machine or Transcendent Mind>
Author: <Hans Moravec> > …
Reviews: <Author: <Amazon.com Reviews> Text: <How much do we humans…> >
Comments: <Stars: <4> Author: <Stephen A. Haines> Text:<Kurzweil has …> >
Related Authors: <Hans P. Moravec> <K. Eric Drexler>…
Subjects: <Science/Mathematics> <Computers> <Artificial Intelligence> …

33. Libra Content Information
Libra uses this extracted information to form “bags of words” for the following slots:
Author
Title
Description (reviews and comments)
Subjects
Related Titles
Related Authors

34. Libra Overview User rates selected titles on a 1 to 10 scale.
Libra uses a naïve Bayesian text-categorization algorithm to learn a profile from these rated examples.
Rating 6–10: Positive
Rating 1–5: Negative

35. LIMITATIONS(Content Based)
Finding the appropriate features
Overspecialization
Never recommends items outside user’s content profile
introduce some randomness
ex: genetic algorithms
the diversity of recommendations is often a desirable feature in recommender systems.
Too similar items should not be recommended,
ex:a different news article describing the same event.

36. LIMITATIONS(Content Based)
Recommendations for new users
How to build a profile?
The user has to rate a sufficient number of items before a content-based recommender system can really understand the user’s preferences. Therefore, a new user, having very few ratings, would not be able to get accurate recommendations.

37. COLLABORATIVE FILTERING

38. Collaborative Filtering
Unlike content-based recommendation methods, collaborative recommender systems (or collaborative filtering systems) try to predict the utility of items based on the items previously rated by other similar users.
The utility u(c,s) of item s for user c is estimated based on the utilities u(c,s) assigned to item s by those users cj є C who are “similar” to user c.

39. Basic Algorithm
Maintain a database of many users’ ratings of a variety of items.
For a given user, find other similar users whose ratings strongly correlate with the current user.
Recommend items rated highly by these similar users, but not rated by the current user.
Almost all existing commercial recommenders use this approach (e.g. Amazon).

40. Similar Users Let rx be the vector of user x’s ratings
Cosine similarity measure
sim(x,y) = cos(rx , ry)
Pearson correlation coefficient
....

41. Collaborative Filtering
: :
Z 5 A B
C 9
Z 10
A 5
Z 7
C 8
: : Z
A 6
B 4
A 10
. .
Z 1
User
Database
Correlation
Match
A 9
B 3 C
: :
Z 5
A 10
B 4
C 8
. .
Z 1 C
A 9
B 3 C
. .
Z 5
Extract
Recommendations
Active
User

42. LIMITATIONS(Collaborative)
New User Problem:
same problem as with content-based systems.
In order to make accurate recommendations, the system must first learn the user’s preferences from the ratings that the user gives.
New Item Problem:
New items are added regularly to recommender systems.
Collaborative systems rely solely on users’ preferences to make recommendations.
Therefore, until the new item is rated by a substantial number of users, the recommender system would not be able to recommend it.
Not a problem in content based!!
Works for any kind of item, No feature selection needed

43. HYBRID METHODS

44. Hybrid Methods
Content-based and collaborative methods have complementary strengths and weaknesses.
Combine methods to obtain the best of both.

45. HOW TO COMBINE?
Implement two separate recommenders and combine predictions, by giving weights
Add content-based methods to collaborative filtering
Use content-based predictor to complete collaborative data.
“Content-Boosted Collaborative Filtering for Improved Recommendations”,Prem Melville and Raymond J. Mooney and Ramadass Nagarajan, 2002,AAAI

46. Movie Domain hybrid approach in the domain of movie recommendation
the user-movie ratings from the EachMovie dataset
The dataset contains rating data provided by each user for various movies.
User ratings range from zero to five stars. Zero stars indicate extreme dislike for a movie and five stars indicate high praise.
The content information for each movie was collected from IMDb using a simple crawler.
The crawler follows the IMDB link provided for every movie in the EachMovie dataset and collects information.
Content information of every movie is represented by a set of slots (features).
Each slot is represented simply as a bag of words.
The slots used for the Each-Movie dataset are: movie title, director, cast, genre, plot

47. Content-Boosted CF - I User-ratings Vector Training Examples
User-rated Items
Unrated Items
Content-Based
Predictor
Training Examples
Pseudo User-ratings Vector
Items with Predicted Ratings

48. Content-Boosted CF - II
User Ratings
Matrix
Pseudo User
Ratings Matrix
Content-Based
Predictor
Compute pseudo user ratings matrix
Full matrix – approximates actual full user ratings matrix
Perform CF
Using Pearson corr. between pseudo user-rating vectors

49. Content-Boosted Collaborative Filtering
EachMovie
Web Crawler
IMDb
User Ratings
Matrix (Sparse)
Content-based
Predictor
Movie
Content
Database
Full User
Ratings Matrix
Collaborative
Filtering
Active
User Ratings
Recommendations

50. PART 4:
CONCLUSION

51. CONCLUSION
Recommendation System is an important technology to combating information overload.
Collaborative filtering has problems.
Content-based methods address these problems (but have problems of their own).
Integrating both is best.

52. THANK YOU FOR LISTENING
QUESTIONS?