The best indicator that a passenger will show up to board the flight is that she called in for a special meal Filtering and Recommender Systems Content-based.
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Filtering and Recommender
SystemsContent-based
and Collaborative
Some of the slides based
On Mooney’s Slides
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– 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
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
– 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
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..
Content/Profile-basedRedMars
Juras-sicPark
LostWorld
2001
Foundation
Differ-enceEngine
Machine Learning
UserProfile
Neuro-mancer
2010
Collaborative Filtering
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UserDatabase
ActiveUser
CorrelationMatch
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ExtractRecommendations
C
Content-based vs. CollaborativeRecommendation
Needs description of items…
Needs only ratings from other users
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
Adapting Naïve Bayes idea for Book Recommendation
• 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– 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. – Related to mutual information
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Collaborative Filtering
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ExtractRecommendations
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Correlation analysis
Here is similar to the
Association clusters
Analysis!
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
– Pearson correlation coefficient is a variation• And find who are most similar users..
– Can do scalar clusters over items etc.. • And find what are most correlated items
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A Collaborative Filtering Method(think kNN)
• 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.
Finding User Similarity with Person Correlation Coefficient
• Typically use Pearson correlation coefficient between ratings for active user, a, and another user, u.
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Person Correlation Coefficient is the same as vector similarity over
centered ratings vectors• It is easy to check for yourself that
pearson correlation coefficient is the same as the cosine theta distance between centered ratings vectors– Covariance = dot product– Sqrt (Variance of each vector) = norm of
each vector
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.
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.
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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.
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Item-centered Collaborative Filtering
• Starting with a “centered” user-item matrix, we found k-nearest users to the active user and used them to recommend unrated items
• We can also use the centered U-I matrix to compute item-item correlations by starting with U-I’xU-I, and doing (a) association clusters and (b) scalar clusters
• This will give us, for each item, k-nearest items– Now, given a new item In to be rated for a user U, we first find k items
closest to In and, and take their (weighted) average rating from the user U as predictive of U’s rating of In
– An advantage of this method over the “user-centered” idea is that the justifications for the recommendations can be more meaningful (you can tell the user that we are recommending In because she rated the items in its association cluster high..)
LSI-style techniques for collaborative filtering
• The NETFLIX prize was won by an approach that did “latent factor analysis” (aka LSI) on the u-i matrix, so that both users and items are seen as vectors in a k-dimensional factor space
• One technical difficulty in doing LSI on u-i matrix is that it has many “null” values– D-t matrix is sparse and that is good. U-I
matrix has null values and that is bad (because null != 0)
• Two approaches:– “fill in” the missing ratings (“Imputation”
method) so we have no more null values– “compute distance between vectors only in
terms of their common non-null dimensions• Problem: Overfitting. Solution:
Regularization—penalize “large factor” values.
qi item in factor spacepu user in factor space
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? #$%$%$&^
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
Content-Based Predictor
Training Examples
Pseudo User-ratings Vector
Items with Predicted Ratings
User-ratings Vector
User-rated Items
Unrated Items
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
• This works better than either!
User RatingsMatrix
Pseudo UserRatings Matrix
Content-BasedPredictor
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?
• (no coincidence—K-means is “hard-assignment” EM)
Unlabeled examples help only when they are drawn from the same distribution as the labeled ones..
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