Filtering and Recommender Systems Content-based and Collaborative

1. Filtering and Recommender SystemsContent-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 Non-Intrusive Intrusive • Explicitly ask users to rate items/pages • 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

6. Justifying Recommendation.. • Recommendation systems must justify their recommendations • Even if the justification is bogus.. • For search engines, the “justifications” are the page synopses • Some recommendation algorithms are better at providing human-understandable justifications than others • Content-based ones can justify in terms of classifier features.. • Collaborative ones are harder-pressed other than saying “people like you seem to like this stuff” • In general, giving good justifications is important..

7. Content-based vs. Collaborative Recommendation

8. User Database 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 A 9 B 3 C . . Z 5 A 9 B 3 C : : Z 5 A 10 B 4 C 8 . . Z 1 Correlation Match Extract Recommendations C Active User Collaborative Filtering 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 users as vectors in the space of items (or vice versa) • Can do vector similarity between users • And find who are most similar users.. • Can do scalar clusters over items etc.. • And find what are most correlated items Think usersdocs Itemskeywords

10. A Collaborative Filtering Method(think kNN regression) • Weight all users with respect to similarity with the active user. • How to measure similarity? • Could use cosine similarity; normally pearson coefficient is used • 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. Today Complete Filtering Discuss Das/Datar paper 3/27 Homework 2 Solns posted Midterm on Thursday in class Covers everything covered by the first two homeworks Qns??

12. Finding User Similarity with Person Correlation Coefficient • 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 botha and u ri,j is user i’s rating for item j

13. 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 k users, u, based on similarity weights, wa,u • Alternate approach is to include all users whose similarity weight is above a given threshold.

14. Rating Prediction • Predict a rating, pa,i, for each item i, for active user, a, by using the k selected neighbor users, u  {1,2,…k}. • 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

15. Similarity Weighting=User Similarity • 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 botha and u ri,j is user i’s rating for item j

16. 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.

17. 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?#$%$%$&^

18. 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

19. 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; Conditional probabilities for each word w.r.t each class and bag Can give a profile of a user in terms of words that are most predictive of what they like 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. Adapting Naïve Bayes idea for Book Recommendation

20. 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!

21. 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.

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

23. 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 • This works better than either!

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? (or how access to unlabelled examples improves accuracy…) • Learn a NBC classifier C0 using the few items for which we have user ratings • Use C0 to predict the ratings for the rest of the items • Loop • Learn a new classifier C1 using all the ratings (real and predicted) • Use C1 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 means that each (unlabelled) entity could belong to multiple classes—with fractional membership in each • We weight the counts by the membership fractions • E.g. P(A=v|c) = Sum of class weights of all examples in c that have A=v divided by Sum of class weights of all examples in c • 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. You train me—I train you… Co-training Small labeled data needed • 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 ~ A2 A1 [x1, x2] <[x1, x2], f1(x1)> f2 Unlabeled Instances Labeled Instances Hypothesis

28. 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. Discussion of the Google News Collaborative Filtering Paper