Search
Session 12
LBSC 690
Information Technology
Agenda
•	The search process

•	Information retrieval

•	Recommender systems

•	Evaluation
Information “Retrieval”
•	Find something that you want
–	The information need may or may not be explicit

•	Known item search
–	Find the class home page

•	Answer seeking
–	Is Lexington or Louisville the capital of Kentucky?

•	Directed exploration
–	Who makes videoconferencing systems?
Information Retrieval Paradigm
Supporting the Search Process
Supporting the Search Process
Human-Machine Synergy
•	Machines are good at:
–	Doing simple things accurately and quickly
–	Scaling to larger collections in sublinear time

•	People are better at:
–	Accurately recognizing what they are looking for
–	Evaluating intangibles such as “quality”

•	Both are pretty bad at:
–	Mapping consistently between words and concepts
Search Component Model
Ways of Finding Text
•	Searching metadata
–	Using controlled or uncontrolled vocabularies

•	Free text
–	Characterize documents by the words the contain

•	Social filtering
–	Exchange and interpret personal ratings
“Exact Match” Retrieval
•	Find all documents with some characteristic
–	Indexed as “Presidents -- United States”
–	Containing the words “Clinton” and “Peso”
–	Read by my boss

•	A set of documents is returned
–	Hopefully, not too many or too few
–	Usually listed in date or alphabetical order
Ranked Retrieval
•	Put most useful documents near top of a list
–	Possibly useful documents go lower in the list

•	Users can read down as far as they like
–	Based on what they read, time available, ...

•	Provides useful results from weak queries
–	Untrained users find exact match harder to use
Similarity-Based Retrieval
•	Assume “most useful” = most similar to query

•	Weight terms based on two criteria:
–	Repeated words are good cues to meaning
–	Rarely used words make searches more selective

•	Compare weights with query
–	Add up the weights for each query term
–	Put the documents with the highest total first
Simple Example: Counting Words
Discussion Point: 
Which Terms to Emphasize?
•	Major factors
–	Uncommon terms are more selective
–	Repeated terms provide evidence of meaning

•	Adjustments
–	Give more weight to terms in certain positions
•	Title, first paragraph, etc.
–	Give less weight each term in longer documents
–	Ignore documents that try to “spam” the index
•	Invisible text, excessive use of the “meta” field, …
“Okapi” Term Weights
Index Quality
•	Crawl quality
–	Comprehensiveness, dead links, duplicate detection
•	Document analysis
–	Frames, metadata, imperfect HTML, …
•	Document extension
–	Anchor text, source authority, category, language, …
•	Document restriction (ephemeral text suppression)
–	Banner ads, keyword spam, …
Indexing Anchor Text
•	A type of “document expansion”
–	Terms near links describe content of the target

•	Works even when you can’t index content
–	Image retrieval, uncrawled links, …
Queries on the Web (1999)
•	Low query construction effort
–	2.35 (often imprecise) terms per query
–	20% use operators
–	22% are subsequently modified

•	Low browsing effort
–	Only 15% view more than one page
–	Most look only “above the fold”
•	One study showed that 10% don’t know how to scroll!
Types of User Needs
•	Informational (30-40% of AltaVista queries)
–	What is a quark?
•	Navigational 
–	Find the home page of United Airlines
•	Transactional	
–	Data: 		What is the weather in Paris?
–	Shopping:	Who sells a Viao Z505RX?
–	Proprietary:	Obtain a journal article
Searching Other Languages

Speech Retrieval Architecture
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
Problems with Explicit Ratings
Implicit Evidence for Ratings
Click Streams
•	Browsing histories are easily captured
–	Send all links to a central site
–	Record from and to pages and user’s cookie
–	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

Estimating Authority from Links
Information Retrieval Types
Hands On: Try Some Search Engines
•	Web Pages (using spatial layout)
–	http://kartoo.com/
•	Images (based on image similarity)
–	http://elib.cs.berkeley.edu/photos/blobworld/
•	Multimedia (based on metadata)
–	http://singingfish.com
•	Movies (based on recommendations)
–	http://www.movielens.umn.edu
•	Grey literature (based on citations)
–	http://citeseer.ist.psu.edu/
Evaluation
•	What can be measured that reflects the searcher’s ability to use a system? (Cleverdon, 1966)
–	Coverage of Information
–	Form of Presentation
–	Effort required/Ease of Use
–	Time and Space Efficiency
–	Recall
–	Precision

Precision-Recall Curves
Affective Evaluation
•	Measure stickiness through frequency of use
–	Non-comparative, long-term
•	Key factors (from cognitive psychology):
–	Worst experience
–	Best experience
–	Most recent experience
•	Highly variable effectiveness is undesirable
–	Bad experiences are particularly memorable
Other Web Search Quality Factors
•	Spam suppression
–	“Adversarial information retrieval”
–	Every source of evidence has been spammed
•	Text, queries, links, access patterns, …

•	“Family filter” accuracy
–	Link analysis can be very helpful
Summary