The document discusses various aspects of search engines and information retrieval systems. It covers topics like how search engines work, indexing content, query processing, relevance ranking, displaying search results and improving search quality. Some key points include how search engines convert information needs to queries, index content ahead of time, match query terms to indexed words, use relevance algorithms to sort results, and factors that influence search quality like content coverage, query clarity and system failures.

2. Helping people find what they’re looking for
 Starts with an “information need”
 Convert to a query
 Gets results
In the materials available
 Web pages
 Other formats
 Deep Web

3.  Search can’t find what’s not there
 The content is hugely important
 Information Architecture is vital
 Usable sites have good navigation and structure

5. Index ahead of time
• Find files or records
• Open each one and read it
• Store each word in a searchable index
Provide search forms
• Match the query terms with words in the index
• Sort documents by relevance
Display results

7. Like an iceberg,
2/3 below water
user
interface
search
content functionality

8. • Text search works for structured content
• Keyword search vs. SQL queries
• Approximate vs. exact match
• Multiple sources of content
• Response time and database resources
• Relevance ranking, very important
• Works in the real world (e.g. EBay)

9. Users blame the search engine
 Even when the content is unavailable
Understand the scope of site or intranet
 Kinds of information
 Divided sites: products / corporate info
 Dates
 Languages
 Sources and data silos: CMSs, databases...
 Update processes

10. Store text to search it later
Many ways to gather text
 Crawl (spider) via HTTP
 Read files on file servers
 Access databases (HTTP or API)
 Data silos via local APIs
 Applications, CMSs, via Web Services
Security and Access Control

12.  Basic information for document or record
• File name / URL / record ID
• Title or equivalent
• Size, date, MIME type
 Full text of item
 More metadata
• Product name, picture ID
• Category, topic, or subject
• Other attributes, for relevance ranking and display

15. Stop words
Stemming
Metadata
 Explicit (tags)
 Implicit (context)
Semantics
 CMS and Database fields
 XML tags and attributes

16. What happens after you click the search button and
before retrieval starts.
Usually in this order
 Handle character set, maybe language
 Look for operators and organize the query
 Look for field names or metadata
 Extract words (just like the indexer)
 Deal with letter casing

17. • Retrieval: find files with query terms
• Not the same as relevance ranking
Recall: find all
relevant items
Precision: find only
relevant items
Increasing one
decreases the
other

18. Single-word queries
 Find items containing that word
Multi-word queries: combine lists
 Any: every item with any query word
 All: only items with every word
 Phrases: find only items with all words in order
Boolean and complex queries
– Use algorithm to combine lists

19. • Empty search
• Nothing on the site on that topic (scope)
• Misspelling or typing mistakes
• Vocabulary differences
• Restrictive search defaults
• Restrictive search choices
• Software failure

21. Theory: sort the matching items, so the most
relevant ones appear first
Can't really know what the user wants
Relevance is hard to define and situational
Short queries tend to be deeply ambiguous
What do people mean when they type “bank”?
First 10 results are the most important
The more transparent, the better

22.  Sorting documents on various criteria
 Start with words matching query terms
 Citation and link analysis
 Like old library Citation Indexes
 Ted Nelson - not only hypertext, but the links
 Google PageRank
 Incoming links
 Authority of linkers
 Taxonomies and external metadata

23. • Term frequency in the item
• Inverse document frequency of term
 Rare words are likely to be more important
wij = weight of Term Tj in Document Di
tfij = frequency of Term Tj in Document Dj
N = number of Documents in collection
n = number of Documents where term Tj
occurs at least once
From Salton 1989

24. • Vector space
• Probabilistic (binary interdependence)
• Fuzzy set theory
• Bayesian statistical analysis
• Latent semantic indexing
• Neural networks
• Machine learning
• All require sophisticated queries
• See MIR, chapter 2

25. Heuristics are rules of thumb
• Not algorithms, not math
Search Relevance Ranking Heuristics
• Documents containing all search words
• Search words as a phrase
• Matches in title tag
• Matches in other metadata
Based on real-word user behavior

26. What users see after they click the Search button
The most visible part of search
Elements of the results page
 Page layout and navigation
 Results header
 List of results items
 Results footer

29. Human judgment beats algorithms
Great for frequent, ambiguous searches
 Use search log to identify best candidates
Recommend good starting pages
 Product information, FAQs, etc.
Requires human resources
 That means money and time
More static than algorithmic search

34.  Leverage content structure
 database fields (i.e. cruise amenities)
 document metadata (news article bylines)
 Provide both search and browse
 Support information foraging
 Integrate navigation with results
 Not just subject taxonomies
 Display only fruitful paths, no dead ends
 Supported by academic research
 Marti Hearst, UCB SIMS, flamenco.berkeley.edu

37. Metrics
 Number of searches
 Number of no-matches searches
 Traffic from search to high-value pages
 Relate search changes to other metrics
Search Log Analysis
 Top 5% searches: phrases and words
 Top no-matches searches
 Use as market research

38. Search engines can’t read minds
 User queries are short and ambiguous
Some things will help
 Design a usable interface
 Show match words in context
 Keep index current and complete
 Adjust heuristic weighting
 Maintain suggestions and synonyms
 Consider faceted metadata search

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