Evidence from Behavior
LBSC 796/CMSC 828o
Douglas W. Oard
Session 5, February 23, 2004
Agenda
•	Questions

•	Observable Behavior

•	Information filtering
Some Observable Behaviors

Some Examples
Estimating Authority from Links
Collecting Click Streams
•	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

More Complete Observations
•	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

Information Access Problems
Information Filtering
Information Filtering
•	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
A Simple Filtering Strategy
•	Use any information retrieval system
–	Boolean, vector space, probabilistic, …

•	Have the user specify a “standing query”
–	This will be the profile

•	Limit the standing query by date
–	Each use, show what arrived since the last use
Social Filtering
•	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
Rating-Based Recommendation
•	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, …
Using Positive Information
Using Negative Information
The Cold Start Problem
•	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
Some Things We (Sort of) Know
•	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
Challenges
•	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 
Motivations to Provide Ratings
•	Self-interest
–	Use the ratings to improve system’s user model

•	Economic benefit
–	If a market for ratings is created

•	Altruism
The Problem With Self-Interest
Solving the Cost vs. Value Problem
•	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
Solution: Reduce the Marginal Cost
Implicit Feedback
•	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, …)
•	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?

Recommending w/Implicit Feedback
Beyond Information Filtering
•	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
Relevance Feedback
Rocchio Formula
Supervised Learning
•	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
Machine Learning Approaches
•	Hill climbing (Rocchio)
•	Instance-based learning 
•	Rule induction
•	Regression
•	Neural networks
•	Genetic algorithms
•	Statistical classification
Statistical Classification
•	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
Rule Induction
•	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, …
Training Strategies
•	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
Critical Issues
•	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?
Gaining Access to Observations
•	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
A More Secure Data Flow
Item
Behavior
Feature
Recommendation
Low Entropy Attack

Matrix Difference Attack

Identity Integrity Attack

One Minute Paper
•	What do you think is the most significant factor that limits the utility of recommender systems?

•	What was the muddiest point in today’s lecture?