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

2. Feature selection & LSI Both MI and LSI are dimensionality reduction techniques MI is looking to reduce dimensions by looking at a subset of the original dimensions –LSI looks instead at a linear combination of the subset of the original dimensions (Good: Can automatically capture sets of dimensions that are more predictive. Bad: the new features may not have any significance to the user) MI does feature selection w.r.t. a classification task (MI is being computed between a feature and a class) –LSI does dimensionality reduction independent of the classes (just looks at data variance) –..where as MI needs to increase variance across classes and reduce variance within class Doing this is called LDA (linear discriminant analysis) LSI is a special case of LDA where each point defines its own class Digression

3. 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).

4. 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

5. 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.

6. Feedback Detection –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 Non-Intrusive Intrusive

7. Content-based vs. Collaborative Recommendation

8. Collaborative Filtering A 9 B 3 C : Z 5 A B C 9 : Z 10 A 5 B 3 C : Z 7 A B C 8 : Z A 6 B 4 C : Z A 10 B 4 C 8. Z 1 User Database Active User Correlation Match A 9 B 3 C. Z 5 A 9 B 3 C : Z 5 A 10 B 4 C 8. Z 1 Extract Recommendations C Correlation analysis Here is similar to the Association clusters Analysis!

9. Item-User Matrix The input to the collaborative filtering algorithm is an mxn matrix where rows are items and columns are users –Sort of like term-document matrix (items are terms and documents are users) Can think of items as vectors in the space of users (or users as vectors in the space of items) –Can do scalar clusters etc..

10. 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.

11. Similarity Weighting Typically use Pearson correlation coefficient between ratings for active user, a, and another user, u. r a and r u are the ratings vectors for the m items rated by both a and u r i,j is user i’s rating for item j

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, w a,u Alternate approach is to include all users whose similarity weight is above a given threshold.

13. Rating Prediction Predict a rating, p a,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. ri,j is user i’s rating for item j

14. Covariance and Standard Deviation Covariance: Standard Deviation:

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

16. 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? #$%$%$&^

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

18. 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!

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. 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.

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

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

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

24. Why can’t the pseudo ratings be used to help content-based filtering? How about using the pseudo ratings to improve a content-based filter itself? –Learn a NBC classifier C 0 using the few items for which we have user ratings –Use C 0 to predict the ratings for the rest of the items –Loop Learn a new classifier C 1 using all the ratings (real and predicted) Use C 1 to (re)-predict the ratings for all the unknown items –Until no change in ratings With a small change, this actually works in finding a better classifier! –Change: Keep the class posterior prediction (rather than just the max class) This is called expectation maximization –Very useful on web where you have tons of data, but very little of it is labelled –Reminds you of K-means, doesn’t it?

26. (boosted) content filtering

27. Co-Training Motivation Learning methods need labeled data –Lots of pairs –Hard to get… (who wants to label data?) But unlabeled data is usually plentiful… –Could we use this instead??????

28. Co-training Suppose each instance has two parts: x = [x1, x2] x1, x2 conditionally independent given f(x) Suppose each half can be used to classify instance  f1, f2 such that f1(x1) = f2(x2) = f(x) Suppose f1, f2 are learnable f1  H1, f2  H2,  learning algorithms A1, A2 Unlabeled Instances [x1, x2] Labeled Instances A1 f2 Hypothesis ~ A2 Small labeled data needed You train me—I train you…

29. Observations Can apply A1 to generate as much training data as one wants –If x1 is conditionally independent of x2 / f(x), –then the error in the labels produced by A1 – will look like random noise to A2 !!! Thus no limit to quality of the hypothesis A2 can make

30. It really works! Learning to classify web pages as course pages –x1 = bag of words on a page –x2 = bag of words from all anchors pointing to a page Naïve Bayes classifiers –12 labeled pages –1039 unlabeled

32. Focussed Crawling Cho paper –Looks at heuristics for managing URL queue –Aim1: completeness –Aim2: just topic pages Prioritize if word in anchor / URL Heuristics: –Pagerank –#backlinks

33. Modified Algorithm Page is hot if: –Contains keyword in title, or –Contains 10 instances of keyword in body, or –Distance(page, hot-page) < 3

34. Results

35. More Results

36. 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.