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How Search Engines Work: A Technology Overview

How Search Engines Work: A Technology Overview. Avi Rappoport Search Tools Consulting www.searchtools.com consult1@searchtools.com UC Berkeley SIMS class 202 September 16, 2004. Purpose of Search Engines. Helping people find what they’re looking for Starts with an “information need”

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How Search Engines Work: A Technology Overview

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  1. How Search Engines Work:A Technology Overview Avi Rappoport Search Tools Consulting www.searchtools.com consult1@searchtools.com UC Berkeley SIMS class 202 September 16, 2004

  2. Purpose of Search Engines • 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 UCB SIMS 202, Sept. 2004 Avi Rappoport, Search Tools Consulting

  3. Search is Not a Panacea • Search can’t find what’s not there • The content is hugely important • Information Architecture is vital • Usable sites have good navigation and structure UCB SIMS 202, Sept. 2004 Avi Rappoport, Search Tools Consulting

  4. Search Looks Simple UCB SIMS 202, Sept. 2004 Avi Rappoport, Search Tools Consulting

  5. But It's Not • 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 UCB SIMS 202, Sept. 2004 Avi Rappoport, Search Tools Consulting

  6. Search Processing UCB SIMS 202, Sept. 2004 Avi Rappoport, Search Tools Consulting

  7. Search is Mostly Invisible Like an iceberg,2/3 below water user interface search functionality content UCB SIMS 202, Sept. 2004 Avi Rappoport, Search Tools Consulting

  8. Text Search vs. Database Query • 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) UCB SIMS 202, Sept. 2004 Avi Rappoport, Search Tools Consulting

  9. Search is Only as Good as the Content • 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 UCB SIMS 202, Sept. 2004 Avi Rappoport, Search Tools Consulting

  10. Making a Searchable Index • 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 UCB SIMS 202, Sept. 2004 Avi Rappoport, Search Tools Consulting

  11. Robot Indexing Diagram Source:James Ghaphery, VCU UCB SIMS 202, Sept. 2004 Avi Rappoport, Search Tools Consulting

  12. What the Index Needs • 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 UCB SIMS 202, Sept. 2004 Avi Rappoport, Search Tools Consulting

  13. Simple Index Diagram UCB SIMS 202, Sept. 2004 Avi Rappoport, Search Tools Consulting

  14. More Complex Index Processing UCB SIMS 202, Sept. 2004 Avi Rappoport, Search Tools Consulting

  15. Index Issues • Stopwords • Stemming • Metadata • Explicit (tags) • Implicit (context) • Semantics • CMS and Database fields • XML tags and attributes UCB SIMS 202, Sept. 2004 Avi Rappoport, Search Tools Consulting

  16. Search Query Processing • 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 UCB SIMS 202, Sept. 2004 Avi Rappoport, Search Tools Consulting

  17. Search and Retrieval • 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 UCB SIMS 202, Sept. 2004 Avi Rappoport, Search Tools Consulting

  18. Retrieval = Matching • 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 UCB SIMS 202, Sept. 2004 Avi Rappoport, Search Tools Consulting

  19. Why Searches Fail • Empty search • Nothing on the site on that topic (scope) • Misspelling or typing mistakes • Vocabulary differences • Restrictive search defaults • Restrictive search choices • Software failure UCB SIMS 202, Sept. 2004 Avi Rappoport, Search Tools Consulting

  20. LII.org No-Matches Page UCB SIMS 202, Sept. 2004 Avi Rappoport, Search Tools Consulting

  21. Relevance Ranking • 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 UCB SIMS 202, Sept. 2004 Avi Rappoport, Search Tools Consulting

  22. Relevance Processing • 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 UCB SIMS 202, Sept. 2004 Avi Rappoport, Search Tools Consulting

  23. TF-IDF Ranking Algorithm • 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 UCB SIMS 202, Sept. 2004 Avi Rappoport, Search Tools Consulting

  24. Other Algorithms • 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 UCB SIMS 202, Sept. 2004 Avi Rappoport, Search Tools Consulting

  25. Relevance Heuristics • 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 UCB SIMS 202, Sept. 2004 Avi Rappoport, Search Tools Consulting

  26. Search Results Interface • 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 UCB SIMS 202, Sept. 2004 Avi Rappoport, Search Tools Consulting

  27. Many Experiments in Interface UCB SIMS 202, Sept. 2004 Avi Rappoport, Search Tools Consulting

  28. Back to Simplicity UCB SIMS 202, Sept. 2004 Avi Rappoport, Search Tools Consulting

  29. Search Suggestions (aka Best Bets) • 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 UCB SIMS 202, Sept. 2004 Avi Rappoport, Search Tools Consulting

  30. MSU Keywords UCB SIMS 202, Sept. 2004 Avi Rappoport, Search Tools Consulting

  31. Siemens Results UCB SIMS 202, Sept. 2004 Avi Rappoport, Search Tools Consulting

  32. Cooks.com Results UCB SIMS 202, Sept. 2004 Avi Rappoport, Search Tools Consulting

  33. Salon.com Results UCB SIMS 202, Sept. 2004 Avi Rappoport, Search Tools Consulting

  34. Faceted Metadata Search & Browse • 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 UCB SIMS 202, Sept. 2004 Avi Rappoport, Search Tools Consulting

  35. Faceted Search: Information UCB SIMS 202, Sept. 2004 Avi Rappoport, Search Tools Consulting

  36. Faceted Search: Online Catalog UCB SIMS 202, Sept. 2004 Avi Rappoport, Search Tools Consulting

  37. Search Metrics and Analytics • 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 UCB SIMS 202, Sept. 2004 Avi Rappoport, Search Tools Consulting

  38. Search Will Never Be Perfect • 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 UCB SIMS 202, Sept. 2004 Avi Rappoport, Search Tools Consulting

  39. Search Engines, sorta Rocket Science • Questions and discussion • Contact me • consult1@searchtools.com • www.searchtools.com  • This presentation: • www.searchtools.com/slides/sims/202-04/ UCB SIMS 202, Sept. 2004 Avi Rappoport, Search Tools Consulting

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