1. Filtering and Recommender Systems Content-based and Collaborative
Some of the slides based
On Mooney’s Slides

2. Personalization
Recommenders are instances of personalization software.
Personalization concerns adapting to the individual needs, interests, and preferences of each user.
Includes:
Recommending
Filtering
Predicting (e.g. form or calendar appt. completion)
From a business perspective, it is viewed as part of Customer Relationship Management (CRM).

3. Feedback & Prediction/Recommendation
Traditional IR has a single user—probably working in single-shot modes
Relevance feedback…
WEB search engines have:
Working continually
User profiling
Profile is a “model” of the user
(and also Relevance feedback)
Many users
Collaborative filtering
Propagate user preferences to other users…
You know this one

4. Recommender Systems in Use
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.

5. Feedback Detection Explicitly ask users to rate items/pages
Non-Intrusive
Intrusive
Click certain pages in certain order while ignore most pages.
Read some clicked pages longer than some other clicked pages.
Save/print certain clicked pages.
Follow some links in clicked pages to reach more pages.
Buy items/Put them in wish-lists/Shopping Carts
Explicitly ask users to rate items/pages

6. Content-based vs. Collaborative
Recommendation

7. 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
Correlation analysis
Here is similar to the
Association clusters
Analysis! C
A 9
B 3 C
. .
Z 5
Extract
Recommendations
Active
User

8. Collaborative Filtering Method
Weight all users with respect to similarity with the active user.
Select a subset of the users (neighbors) to use as predictors.
Normalize ratings and compute a prediction from a weighted combination of the selected neighbors’ ratings.
Present items with highest predicted ratings as recommendations.

9. Similarity Weighting
Typically use Pearson correlation coefficient between ratings for active user, a, and another user, u.
ra and ru are the ratings vectors for the m items rated by
both a and u
ri,j is user i’s rating for item j
Where did you see this stuff?
Association clusters…

10. Covariance and Standard Deviation

11. Significance Weighting
Important not to trust correlations based on very few co-rated items.
Include significance weights, sa,u, based on number of co-rated items, m.

12. Neighbor Selection
For a given active user, a, select correlated users to serve as source of predictions.
Standard approach is to use the most similar n users, u, based on similarity weights, wa,u
Alternate approach is to include all users whose similarity weight is above a given threshold.

13. Rating Prediction
Predict a rating, pa,i, for each item i, for active user, a, by using the n selected neighbor users,
u  {1,2,…n}.
To account for users different ratings levels, base predictions on differences from a user’s average rating.
Weight users’ ratings contribution by their similarity to the active user.

14. Problems with Collaborative Filtering
Cold Start: There needs to be enough other users already in the system to find a match.
Sparsity: If there are many items to be recommended, even if there are many users, the user/ratings matrix is sparse, and it is hard to find users that have rated the same items.
First Rater: Cannot recommend an item that has not been previously rated.
New items
Esoteric items
Popularity Bias: Cannot recommend items to someone with unique tastes.
Tends to recommend popular items.
WHAT DO YOU MEAN YOU DON’T CARE FOR BRITNEY SPEARS YOU DUNDERHEAD? #$%$%$&^

15. October 28th --Exam lament (I mean discussion) --Project 1 due date…
--Continuation of Recommender Systems

16. Content-Based Recommending
Recommendations are based on information on the content of items rather than on other users’ opinions.
Uses a machine learning algorithm to induce a profile of the users preferences from examples based on a featural description of content.
Lots of systems

17. Advantages of Content-Based Approach
No need for data on other users.
No cold-start or sparsity problems.
Able to recommend to users with unique tastes.
Able to recommend new and unpopular items
No first-rater problem.
Can provide explanations of recommended items by listing content-features that caused an item to be recommended.
Well-known technology The entire field of Classification Learning is at (y)our disposal!

18. Tucson Citizen, October 30th, 2002
[NRA Tucson Board Member, Todd] Rathner was critical of UA's ban on firearms, saying the policy puts students and faculty at risk to dangerous individuals - such as the student who shot three UA College of Nursing instructors before committing suicide. "They have made the UA an unarmed-victims zone," he said
Tucson Citizen, October 30th, 2002
October 30th

19. Disadvantages of Content-Based Method
Requires content that can be encoded as meaningful features.
Users’ tastes must be represented as a learnable function of these content features.
Unable to exploit quality judgments of other users.
Unless these are somehow included in the content features.

20. Primer on Classification Learning
FAST
Primer on Classification Learning
(you can learn more about this in
CSE 471 Intro to AI
CSE 575 Datamining
EEE 511 Neural Networks)

21. Many uses of Classification Learning in IR/Web Search
Learn user profiles
Classify documents into categories based on their contents
Useful in
focused crawling
Topic-sensitive page rank

22. A classification learning example
Predicting when Rusell will wait for a table
--similar to book preferences, predicting credit card fraud,
predicting when people are likely to respond to junk mail

23. Uses different biases in predicting Russel’s waiting habbits
Decision Trees
--Examples are used to
--Learn topology
--Order of questions
K-nearest
neighbors
If patrons=full and day=Friday
then wait (0.3/0.7)
If wait>60 and Reservation=no
then wait (0.4/0.9)
Association rules
--Examples are used to
--Learn support and
confidence of association
rules
SVMs
Neural Nets
--Examples are used to
--Learn topology
--Learn edge weights
Naïve bayes
(bayesnet learning)
--Examples are used to
--Learn topology
--Learn CPTs

24. Mirror, Mirror, on the wall Which learning bias is the best of all?
Well, there is no such thing, silly!
--Each bias makes it easier to learn some patterns and harder (or impossible) to learn others:
-A line-fitter can fit the best line to the data very fast but won’t know what to do if the data doesn’t fall on a line
--A curve fitter can fit lines as well as curves… but takes longer time to fit lines than a line fitter.
-- Different types of bias classes (Decision trees, NNs etc) provide different ways of naturally carving up the space of all possible hypotheses
So a more reasonable question is:
-- What is the bias class that has a specialization corresponding to the type of patterns that underlie my data?
-- In this bias class, what is the most restrictive bias that still can capture the true pattern in the data?
--Decision trees can capture all boolean functions
--but are faster at capturing conjunctive boolean functions
--Neural nets can capture all boolean or real-valued functions
--but are faster at capturing linearly seperable functions
--Bayesian learning can capture all probabilistic dependencies
But are faster at capturing single level dependencies (naïve bayes classifier)

25. Fitting test cases vs. predicting future cases
The BIG TENSION…. 2 1
Why Simple is Better? 3
Why not the 3rd?

26. Naïve Bayesian Classification
Problem: Classify a given example E into one of the classes among [C1, C2 ,…, Cn]
E has k attributes A1, A2 ,…, Ak and each Ai can take d different values
Bayes Classification: Assign E to class Ci that maximizes P(Ci | E)
P(Ci| E) = P(E| Ci) P(Ci) / P(E)
P(Ci) and P(E) are a priori knowledge (or can be easily extracted from the set of data)
Estimating P(E|Ci) is harder
Requires P(A1=v1 A2=v2….Ak=vk|Ci)
Assuming d values per attribute, we will need ndk probabilities
Naïve Bayes Assumption: Assume all attributes are independent P(E| Ci) = P P(Ai=vj | Ci )
The assumption is BOGUS, but it seems to WORK (and needs only n*d*k probabilities

27. Estimating the probabilities for NBC
Given an example E described as A1=v1 A2=v2….Ak=vk we want to compute the class of E
Calculate P(Ci | A1=v1 A2=v2….Ak=vk) for all classes Ci and say that the class of E is the one for which P(.) is maximum
P(Ci | A1=v1 A2=v2….Ak=vk)
= P P(vj | Ci ) P(Ci) / P(A1=v1 A2=v2….Ak=vk)
Given a set of training N examples that have already been classified into n classes Ci
Let #(Ci) be the number of examples that are labeled as Ci
Let #(Ci, Ai=vi) be the number of examples labeled as Ci
that have attribute Ai set to value vj
P(Ci) = #(Ci)/N
P(Ai=vj | Ci) = #(Ci, Ai=vi) / #(Ci)
Common factor
USER PROFILE

28. Example
P(willwait=yes) = 6/12 = .5
P(Patrons=“full”|willwait=yes) = 2/6=0.333
P(Patrons=“some”|willwait=yes)= 4/6=0.666
Similarly we can show that
P(Patrons=“full”|willwait=no)
=0.6666
P(willwait=yes|Patrons=full) = P(patrons=full|willwait=yes) * P(willwait=yes)
P(Patrons=full)
= k* .333*.5
P(willwait=no|Patrons=full) = k* 0.666*.5

29. Using Naïve Bayes for Text Classification ( “Bag of Words” model)
What are the features in Text?
Use unigram model
Assume that words from a fixed vocabulary V appear in the document D at different positions (assume D has L words)
P(D|C) is P(p1=w1,p2=w2…pL=wl | C)
Assume that words appearance probabilities are independent of each other
P(D|C) is P(p1=w1|C)*P(p2=w2|C) …*P(pL=wl | C)
Assume that word occurrence probability is INDEPENDENT of its position in the document
P(p1=w1|C)=P(p2=w1|C)=…P(pL=w1|C)
Use m-estimates; set p to 1/V and m to V (where V is the size of the vocabulary)
P(wk|Ci) = [#(wk,Ci) + 1]/#w(Ci) + V
#(wk,Ci) is the number of times wk appears in the documents classified into class Ci
#w(Ci) is the total number of words in all documents

30. Happy Deepawali!
4th
4th Nov, 2002.

31. Using M-estimates to improve probablity estimates
The simple frequency based estimation of P(Ai=vj|Ck) can be inaccurate, especially when the true value is close to zero, and the number of training examples is small (so the probability that your examples don’t contain rare cases is quite high)
Solution: Use M-estimate
P(Ai=vj | Ci) = [#(Ci, Ai=vi) + mp ] / [#(Ci) + m]
p is the prior probability of Ai taking the value vi
If we don’t have any background information, assume uniform probability (that is 1/d if Ai can take d values)
m is a constant—called “equivalent sample size”
If we believe that our sample set is large enough, we can keep m small. Otherwise, keep it large.
Essentially we are augmenting the #(Ci) normal samples with m more virtual samples drawn according to the prior probability on how Ai takes values

32. Extensions to Naïve Bayes idea
Vector of Bags model
E.g. Books have several different fields that are all text
Authors, description, …
A word appearing in one field is different from the same word appearing in another
Want to keep each bag different—vector of m Bags
Additional useful terms
Odds Ratio
P(rel|example)/P(~rel|example)
An example is positive if the odds ratio is > 1
Strengh of a keyword
Log[P(w|rel)/P(w|~rel)]
We can summarize a user’s profile in terms of the words that have strength above some threshold.

33. How Well (and WHY) DOES NBC WORK?
Naïve bayes classifier is darned easy to implement
Good learning speed, classification speed
Modest space storage
Supports incrementality
Recommendations re-done as more attribute values of the new item become known.
It seems to work very well in many scenarios
Peter Norvig, the director of Machine Learning at GOOGLE said, when asked about what sort of technology they use “Naïve bayes”
But WHY?
[Domingoes/Pazzani; 1996] showed that NBC has much wider ranges of applicability than previously thought (despite using the independence assumption)
classification accuracy is different from probability estimate accuracy

34. Combining Content and Collaboration
Content-based and collaborative methods have complementary strengths and weaknesses.
Combine methods to obtain the best of both.
Various hybrid approaches:
Apply both methods and combine recommendations.
Use collaborative data as content.
Use content-based predictor as another collaborator.
Use content-based predictor to complete collaborative data.

35. Movie Domain EachMovie Dataset [Compaq Research Labs]
Contains user ratings for movies on a 0–5 scale.
72,916 users (avg. 39 ratings each).
1,628 movies.
Sparse user-ratings matrix – (2.6% full).
Crawled Internet Movie Database (IMDb)
Extracted content for titles in EachMovie.
Basic movie information:
Title, Director, Cast, Genre, etc.
Popular opinions:
User comments, Newspaper and Newsgroup reviews, etc.

36. 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

37. 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

38. 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

39. Conclusions
Recommending and personalization are important approaches to combating information over-load.
Machine Learning is an important part of systems for these tasks.
Collaborative filtering has problems.
Content-based methods address these problems (but have problems of their own).
Integrating both is best.