Filtering and Recommender Systems Content-based and Collaborative ed s bas e e slid des h e of t y’s Sli Som oone On M
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
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).
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 You know this one – Profile is a “model” of the user • (and also Relevance feedback) – Many users • Collaborative filtering – Propagate user preferences to other users…
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.
Feedback Detection Non-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 Intrusive – Explicitly ask users to rate items/pages
Content-based vs. Collaborative Recommendation
Collaborative Filtering User Database A B C : Z 9 3 : 5 lysis a n an to the tio rela imilar ters Cor is s s re tion clu He cia o Ass ysis! l Ana Active User A B C 9 : : Z 10 A B C : Z 5 3 A B C 8 : : Z : 7 Correlation Match A 9 B 3 C. . Z 5 A 6 B 4 C : : Z A B C : Z 9 3 : 5 A 10 B 4 C 8. . Z 1 Extract Recommendations C
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. .
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.
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
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.
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. ri, j is user i’s rating for item j
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.
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? #$%$%$&^
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
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!
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.
Content-Boosted CF - I User-ratings Vector Training Examples Content-Based Predictor Pseudo User-ratings Vector User-rated Items Unrated Items with Predicted Ratings
Content-Boosted CF - II User Ratings Matrix Content-Based Predictor Pseudo User Ratings Matrix • Compute pseudo user ratings matrix – Full matrix – approximates actual full user ratings matrix • Perform CF – Using Pearson corr. between pseudo user-rating vectors
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?
(boosted) content filtering
You train me—I train you… Co-training Small labeled data needed • Suppose each instance has two parts: x = [x 1, x 2] x 1, x 2 conditionally independent given f(x) • Suppose each half can be used to classify instance f 1, f 2 such that f 1(x 1) = f 2(x 2) = f(x) • Suppose f 1, f 2 are learnable f 1 H 1, [x 1, x 2] f 2 H 2, A 1 Unlabeled Instances learning algorithms A 1, A 2 <[x 1, x 2], f 1(x 1)> Labeled Instances A 2 ~ f 2 Hypothesis
Observations • Can apply A 1 to generate as much training data as one wants – If x 1 is conditionally independent of x 2 / f(x), – then the error in the labels produced by A 1 – will look like random noise to A 2 !!! • Thus no limit to quality of the hypothesis A 2 can make
It really works! • Learning to classify web pages as course pages – x 1 = bag of words on a page – x 2 = bag of words from all anchors pointing to a page • Naïve Bayes classifiers – 12 labeled pages – 1039 unlabeled
Focussed Crawling • Cho paper – Looks at heuristics for managing URL queue – Aim 1: completeness – Aim 2: just topic pages • Prioritize if word in anchor / URL • Heuristics: – Pagerank – #backlinks
Modified Algorithm • Page is hot if: – Contains keyword in title, or – Contains 10 instances of keyword in body, or – Distance(page, hot-page) < 3
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