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- LBSC 796/CMSC 828o
- Douglas W. Oard
- Session 5, February 23, 2004
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- Questions
- Observable Behavior
- Information filtering
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- Browsing histories are easily captured
- Make all links initially point to a central site
- Encode the desired URL as a parameter
- Build a time-annotated transition graph for each user
- Cookies identify users (when they use the same machine)
- Redirect the browser to the desired page
- Reading time is correlated with interest
- Can be used to build individual profiles
- Used to target advertising by doubleclick.com
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- User selects an article
- Interpretation: Summary was interesting
- User quickly prints the article
- Interpretation: They want to read it
- User selects a second article
- Interpretation: another interesting summary
- User scrolls around in the article
- Interpretation: Parts with high dwell time and/or repeated revisits=
are
interesting
- User stops scrolling for an extended period
- Interpretation: User was interrupted
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- An abstract problem in which:
- The information need is stable
- Characterized by a “profile”
- A stream of documents is arriving
- Each must either be presented to the user or not
- Introduced by Luhn in 1958
- As “Selective Dissemination of Information”
- Named “Filtering” by Denning in 1975
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- Use any information retrieval system
- Boolean, vector space, probabilistic, …
- Have the user specify a “standing query”
- Limit the standing query by date
- Each use, show what arrived since the last use
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- Exploit ratings from other users as features
- Like personal recommendations, peer review, …
- Reaches beyond topicality to:
- Accuracy, coherence, depth, novelty, style, …
- Applies equally well to other modalities
- Movies, recorded music, …
- Sometimes called “collaborative” filtering
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- Use ratings as to describe objects
- Personal recommendations, peer review, …
- Beyond topicality:
- Accuracy, coherence, depth, novelty, style, …
- Has been applied to many modalities
- Books, Usenet news, movies, music, jokes, beer, …
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- Social filtering will not work in isolation
- Without ratings, we get no recommendations
- Without recommendations, we read nothing
- Without reading, we get no ratings
- An initial recommendation strategy is needed
- Stereotypes
- Content-based search
- The need for both leads to hybrid strategies
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- Treating each genre separately can be useful
- Separate predictions for separate tastes
- Negative information can be useful
- “I hate everything my parents like”
- People like to know who provided ratings
- Popularity provides a useful fallback
- People don’t like to provide ratings
- Few experiments have achieved sufficient scale
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- Any form of sharing necessarily incurs:
- Distribution costs
- Privacy concerns
- Competitive concerns
- Requiring explicit ratings also:
- Increases the cognitive load on users
- Can adversely affect ease-of-use
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- Self-interest
- Use the ratings to improve system’s user model
- Economic benefit
- If a market for ratings is created
- Altruism
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- Maximize the value
- Provide for continuous user model adaptation
- Minimize the costs
- Use implicit feedback rather than explicit ratings
- Minimize privacy concerns through encryption
- Build an efficient scalable architecture
- Limit the scope to noncompetitive activities
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- Observe user behavior to infer a set of ratings
- Examine (reading time, scrolling behavior, …)
- Retain (bookmark, save, save & annotate, print, …)
- Refer to (reply, forward, include link, cut & paste, …)=
li>
- Some measurements are directly useful
- e.g., use reading time to predict reading time
- Others require some inference
- Should you treat cut & paste as an endorsement?
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- Citation indexing
- Exploits reference behavior
- Search for people based on their behavior
- Discovery of potential collaborators
- Collaborative data mining in large collections
- Discoveries migrate to people with similar interests
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- Given a set of vectors with associated values
- e.g., term vectors with relevance judgments
- Predict the values associated with new vectors
- i.e., learn a mapping from vectors to values
- All learning systems share two problems
- They need some basis for making predictions
- This is called an “inductive bias”
- They must balance adaptation with generalization
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- Hill climbing (Rocchio)
- Instance-based learning
- Rule induction
- Regression
- Neural networks
- Genetic algorithms
- Statistical classification
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- Represent relevant docs as one random vector
- And nonrelevant docs as another
- Build a statistical model for each distribution
- e.g., model each with mean and covariance
- Find the surface separating the distributions
- e.g., a hyperplane for linear discriminant analysis
- Rank documents by distance from that surface
- Possibly based on the shape of the distributions
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- Automatically derived Boolean profiles
- (Hopefully) effective and easily explained
- Specificity from the “perfect query”
- AND terms in a document, OR the documents
- Generality from a bias favoring short profiles
- e.g., penalize rules with more Boolean operators
- Balanced by rewards for precision, recall, …
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- Overtraining can hurt performance
- Performance on training data rises and plateaus
- Performance on new data rises, then falls
- One strategy is to learn less each time
- But it is hard to guess the right learning rate
- Splitting the training set is a useful alternative
- Part provides the content for training
- Part for assessing performance on unseen data
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- Protecting privacy
- What absolute assurances can we provide?
- How can we make remaining risks understood?
- Scalable rating servers
- Is a fully distributed architecture practical?
- Non-cooperative users
- How can the effect of spamming be limited?
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- Observe public behavior
- Hypertext linking, publication, citing, …
- Policy protection
- EU: Privacy laws
- US: Privacy policies + FTC enforcement
- Architectural assurance of privacy
- Distributed architecture
- Model and mitigate privacy risks
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- Item
- Behavior
- Feature
- Recommendation
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- What do you think is the most significant factor that limits the uti=
lity
of recommender systems?
- What was the muddiest point in today’s lecture?
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