1. INFM 700: Session 9 Search (Part II) Search Engines in Information Architecture Paul Jacobs The iSchool University of Maryland Wednesday, Apr. 18, 2012 This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States See http://creativecommons.org/licenses/by-nc-sa/3.0/us/ for detailshttp://creativecommons.org/licenses/by-nc-sa/3.0/us/

2. iSchool Today’s Topics Very short recap Fundamentals of information retrieval Search engines in practice (web search and web sites) Issues and tricks Stemming/word issues Query formulation/expansion/assistance Tagging/structuring Others Deploying search – what we get to do, and how Issues and Tricks Deploying Search

3. iSchool Vector Space Model Assumption: Documents that are “close together” in vector space “talk about” the same things t1t1 d2d2 d1d1 d3d3 d4d4 d5d5 t3t3 t2t2 θ φ Therefore, retrieve documents based on how close the document is to the query (i.e., similarity ~ “closeness”)

4. iSchool Term Weighting Term weights consist of two components Local: how important is the term in this doc? Global: how important is the term in the collection? Here’s the intuition: Terms that appear often in a document should get high weights Terms that appear in many documents should get low weights How do we capture this mathematically? Term frequency (local) Inverse document frequency (global)

5. iSchool TF.IDF Term Weighting weight assigned to term i in document j number of occurrence of term i in document j number of documents in entire collection number of documents with term i

6. iSchool Summary thus far… Represent documents (and queries) as “bags of words” (terms) Derive term weights based on frequency Use weighted term vectors for each document, query Compute a vector-based similarity score Display sorted, ranked results

7. iSchool Issues and Tricks What’s a word/term? We can ignore words (“stop words”), combine (phrases), split up (“stem”) words Other special treatment (e.g. names, categories) Query formulation/suggestion Type of information need Popularity Based on link analysis/page rank Based on click through, other Structuring and tagging (e.g., “best bets”) Issues and Tricks Deploying Search

8. iSchool Issues and Tricks (cont’d) Thesaurus/query expansion Based on meaning, conceptual relationships Based on decomposition/type User feedback/”More like this” Clustering/grouping of results Issues and Tricks Deploying Search

9. iSchool Morphological Variation Handling morphology: related concepts have different forms Inflectional morphology: same part of speech Derivational morphology: different parts of speech Different morphological processes: Prefixing Suffixing Infixing Reduplication dogs = dog + PLURAL broke = break + PAST destruction = destroy + ion researcher = research + er Issues and Tricks Deploying Search

10. iSchool Stemming Dealing with morphological variation: index stems instead of words Stem: a word equivalence class that preserves the central concept How much to stem? organization  organize  organ? resubmission  resubmit/submission  submit? reconstructionism? Issues and Tricks Deploying Search

11. iSchool Does Stemming Work? Generally, yes! (in English) Helps more for longer queries, fewer results Lots of work done in this area But used very sparingly in web search – why? Donna Harman (1991) How Effective is Suffixing? Journal of the American Society for Information Science, 42(1):7-15. Robert Krovetz. (1993) Viewing Morphology as an Inference Process. Proceedings of SIGIR 1993. David A. Hull. (1996) Stemming Algorithms: A Case Study for Detailed Evaluation. Journal of the American Society for Information Science, 47(1):70-84. And others… Issues and Tricks Deploying Search

12. iSchool Beyond Words… Stemming/tokenization = specific instance of a general problem: what is it? Other units of indexing Concepts (e.g., from WordNet) Named entities Relations … Issues and Tricks Deploying Search

13. iSchool Some Observations Search engine fundamentals are very similar There are many tricks, differences beyond the basic model Differences appear differently, and are magnified as we get to sites, specific applications So, as we get to deployment … Be skeptical Test rigorously Some small things can make a big difference Issues and Tricks Deploying Search

14. iSchool Deployment - Overview What we can control Basic process of setting up/using search in IA Key parameters/issues What to search/organization content Testing and improving results Presentation/interfaces Issues and Tricks Deploying Search

15. iSchool What we control (the IA part)? Requirements and search engine selection Developing search requirements Build vs. buy Vendor evaluation/selection Consultants? Content selection What to search/zones/etc. Tags Search engine configuration Zones, what gets indexed, sometimes how Number of results, sometimes recall vs. precision Others (very often interface-related) Interfaces Issues and Tricks Deploying Search

16. iSchool Search Engine Selection Commercial examples Autonomy (including the former Verity, Ultraseek,...) Google (site search, search appliance) Thunderstone Build your own, open source? Lucene Defining requirements Basic search – how big, type of documents, what sort of interface, metadata, parametric? Advanced requirements – automatic tagging, alerts, “more like this” Customization and improvement using logs Keep it focused? Issues and Tricks Deploying Search

17. iSchool Search Engine Selection (con’d) Pitfalls to avoid “Getting a bargain” Getting it “free” Great sales reps Good ideas Get case studies, talk to references Get a “proof of concept period” Issues and Tricks Deploying Search

18. iSchool Simple Requirements Matrix Issues and Tricks Deploying Search Vendor Name Requirement/CriterionPriorityRatingComments 1. Identify Early Warnings/Search 1.a. Highly detailed information needs1 1.b. Date range restrictions1 1.c. Company name restrictions1 1.d. Alias/equivalence (e.g The Walt Disney Company = Disney)2 1.e. Ability to assign unique IDs (e.g. Disney = NYSE:DIS)2 1 f. Restrict/search by subject area/topic2 1.g. Ability to partition/segment articles with multiple topics3 1.h. Federated search w/web content, Nexis, etc.2 1.i. Use of extended lists (e.g. lists of companies, subjects)2 2. Identify Early Warnings/Alerts 2.a. Highly detailed information needs (all of i-h above)1 2.b. Controlling/weighting specific elements2 2.c. Recall/precision tradeoff3 2.d. Identify "new" and "hot" articles that match user's interest2 2.e. Sentiment analysis component3 3. Identify Early Warnings/Discovery 3.a. Classify documents in pre-defined or user-defined categories3 3.b. Document clustering3 3.c. Identification of trends/issues2 3.d. Other discovery tools3 4. Integration and interface requirements

19. iSchool Content Selection (What to Search) Generally, search everything but … Be leery about providing “search the web” option Use zones or separate text databases for frequent/infrequent information needs Be careful about outdated/deleted content Make sure “best bets” come to the top Use logs, test & improve Issues and Tricks Deploying Search

20. iSchool Testing and Improvement Keep track of queries (and results, if possible) using logs If logs are not available, try user experiments If results are not available, get them Relevance/correct judgments; quantitative (e.g. recall/precision) scores are, too How to improve Focus on most frequent (important?) requests (90-10 or 80-20) “Best bets” Content manipulation (e.g., adding tags) Thesaurus Keep testing Issues and Tricks Deploying Search

21. iSchool “Best Bets” – How to Implement Identify desired result page Determine possible query strings (from logs) Tag meta-data in documents with query string Configure search interface (e.g., to show Best Best first, what to do about multiple Best Bets) This is a special case of using tag field (e.g., keywords, categories, description) Issues and Tricks Deploying Search

22. iSchool Designing a Search Interface The Box (size, position, labels) Content selection (defaults, radio buttons or pull- down selection) Parameters or advanced search (Booleans, separate zones, other possibilities) Issues and Tricks Deploying Search

23. iSchool Designing a Search Interface - Results Number of results to display Recall/precision tradeoff? Snippet/summary information for each hit Layout of best bits/other hits Repetition of the query “No results” – other possible tips Iteration and refinement Other (e.g., scores, clusters, …) Issues and Tricks Deploying Search

24. iSchool Some example sites Issues and Tricks Deploying Search www.hp.com www.dell.com www.ecoearth.info www.washingtonpost.com www.dailygazette.com www.friendsofrockcreek.org www.cbf.org www.umd.edu

25. iSchool Integrating Search and Browsing Provide more navigation for common needs …based on search logs, other info Redirect from search results to navigation Faceted browsing...

26. iSchool Faceted Browsing Example Issues and Tricks Deploying Search

27. iSchool Faceted Browsing Example Issues and Tricks Deploying Search

28. iSchool Faceted Browsing Example Demo: http://flamenco.berkeley.edu/demos.html Issues and Tricks Deploying Search

29. iSchool Advantages of Facets Integrates searching and browsing Easy to build complex queries Easy to narrow, broaden, shift focus Helps users avoid getting lost Helps to prevent “categorization wars” Issues and Tricks Deploying Search

30. iSchool Recap Search is an IA issue! Quality of search results/user experience depends on: Understanding how search engines work Choosing and deploying carefully Constant testing and improvement Time Tremendous range of parameters/interface choices Integrating search and browsing/navigation is a very good idea Issues and Tricks Deploying Search