1. Information Filtering LBSC 878 Douglas W. Oard and Dagobert Soergel Week 10: April 12, 1999

2. Agenda Information filtering Profile learning Social filtering

3. Information Access Problems Collection Information Need Stable Different Each Time Retrieval Filtering Different Each Time

4. 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

5. 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

6. What’s Wrong With That? Unnecessary indexing overhead –Indexing only speeds up retrospective searches Every profile is treated separately –The same work might be done repeatedly Forming effective queries by hand is hard –The computer might be able to help It is OK for text, but what about audio, video, … –Are words the only possible basis for filtering?

7. The Fast Data Finder Fast Boolean filtering using custom hardware –Up to 10,000 documents per second Words pass through a pipeline architecture –Each element looks for one word goodpartygreataid OR AND NOT

8. Profile Indexing in SIFT Build an inverted file of profiles –Postings are profiles that contain each term RAM can hold 5,000 profiles/megabyte –And several machines can be run in parallel Both Boolean and vector space matching –User-selected threshold for each ranked profile Hand-tuned on a web page using today’s news Delivered by email

9. SIFT Limitations Centralization –Requires some form of cost sharing Privacy –Centrally registered profiles –Each profile associated with an email address Usability –Manually specified profiles –Thresholds based on obscure retrieval status value

10. Profile Learning Automatic learning for stable profiles –Based on observations of user behavior Explicit ratings are easy to use –Binary ratings are simply relevance feedback Unobtrusive observations are easier to get –Reading time, save/delete, … –But we only have a few clues on how to use them

11. Relevance Feedback Make Profile Vector Compute Similarity Select and Examine (user) Assign Ratings (user) Update User Model New Documents Vector Documents, Vectors, Rank Order Document, Vector Rating, Vector Vector(s) Make Document Vectors Initial Profile Terms Vectors

12. Machine Learning Given a set of vectors with associated values –e.g., document vector + 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

13. Machine Learning Techniques Hill climbing (Rocchio Feedback) Instance-based learning Rule induction Regression Neural networks Genetic algorithms Statistical classification

14. Instance-Based Learning Remember examples of desired documents –Dissimilar examples capture range of possibilities Compare new documents to each example –Vector similarity, probabilistic, … Base rank order on the most similar example May be useful for broader interests

15. 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, …

16. Regression Fit a simple function to the observations –Straight line, s-curve, … Logistic regression matches binary relevance –S-curves fit better than straight lines –Same calculation as a 3-layer neural network But with a different training technique

17. Neural Networks Design inspired by the human brain –Neurons, connected by synapses Organized into layers for simplicity –Synapse strengths are learned from experience Seek to minimize predict-observe differences Can be fast with special purpose hardware –Biological neurons are far slower than computers Work best with application-specific structures –In biology this is achieved through evolution

18. Genetic Algorithms Design inspired by evolutionary biology –Parents exchange parts of genes (crossover) –Random variations in each generation (mutation) –Fittest genes dominate the population (selection) Genes are vectors –Term weights, source weights, module weights, … Selection is based on a fitness function –Tested repeatedly - must be informative and cheap

19. Statistical Classification Represent relevant docs as one random vector –And nonrelevant docs as another Build a statistical model for each –e.g., a normal distribution Find the surface separating the distributions –e.g., a hyperplane Rank documents by distance from that surface –Possibly distorted by the shape of the distributions

20. 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 for training, part for finding “new data” peak

21. 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

22. Social Filtering Example 96198357 5755549729 3391249818 8259 ComedyDramaActionMystery Amy Norina Paul Skip

23. Some Things We (Sort of) Know Treating each genre separately can be useful –Separate predictions for separate tastes Negative information might 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

24. 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?

25. Constructing a Rating Matrix 1234 Document Jack Sam Tim Ken Marty Joe Jill Mary 0.29 0.37 0.53 0.13 0.62 0.77 0.57 0.14 0.19 0.79 0.05 0.71 0.69 0.44 0.57 Jack Sam Tim Ken Marty Joe Jill Mary Examination Retention Reference Explicit Rating Rater Observation 1234 Document Rater

26. Some Research Questions How readers can pay raters efficiently How to use implicit feedback –No large scale integrated experiments yet How to protect privacy –Pseudonyms can be defeated fairly easily How to merge social and content-based filtering –Social filtering never finds anything new How to evaluate social filtering effectiveness

27. 0.29 0.37 0.53 0.13 0.62 0.77 0.57 0.14 0.19 0.79 0.05 0.71 123 0.69 0.44 0.57 4 0.29 0.37 0.53 0.13 0.62 0.77 0.57 0.14 0.19 0.79 0.05 0.71 0.69 0.44 0.57 Jack Sam Tim Ken Marty Joe Jill Mary Rater nuclear fallout siberia contaminated interesting complicated information retrieval Terms Document Combining Content and Ratings Two sources of features –Terms and raters Each is used differently –Find terms in documents –Find informative raters

28. Variations on Social Filtering Citation indexing –A special case of refer-to implicit feedback Search for people based on their behavior –Discover potential collaborators –Focus advertising on interested consumers Collaborative exploration –Explore a large, fairly stable collection –Discoveries migrate to people with similar interests