Web 2.0 Technologies

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Published on October 30, 2007

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Web 2.0, Tagging, Search engines, RawSugar:  Web 2.0, Tagging, Search engines, RawSugar Frank Smadja RawSugar May 2006 What is Web 2.0:  What is Web 2.0 Tim O’Reilly: Web 2.0 is the network as platform, spanning all connected devices; Web 2.0 applications are those that make the most of the intrinsic advantages of that platform: delivering software as a continually-updated service that gets better the more people use it, consuming and remixing data from multiple sources, including individual users, while providing their own data and services in a form that allows remixing by others, creating network effects through an "architecture of participation," and going beyond the page metaphor of Web 1.0 to deliver rich user experiences. http://www.oreillynet.com/pub/a/oreilly/tim/news/2005/09/30/what-is-web-20.html What is Web 2.0?:  What is Web 2.0? Social Web – “Wisdom of Crowds” Users are publishers Network effect – SHARE - e.g: blogger.com, flickr, youtube, del.icio.us, tadalist.com, i4giveu.com, Technology: Software delivery: Hours, Users are testers AJAX (more later) E.g.: 30Boxes, Writely, Google Calendar Business model: Free for users, Paid Advertisements Share revenues with users E.g., Google adsense, simpy, RawSugar Pageviews => $$$$ Social Web – Wisdom of Crowds:  Social Web – Wisdom of Crowds diversity of opinion independence of members from one another decentralization and a good method for aggregating opinions Show: Digg amazon.com Yahoo! Movies What is Tagging?:  What is Tagging? From Gary Larson Tagging Example:  Tagging Example Before Tagging: Classification:  Before Tagging: Classification Too hard to classify Too expensive Not scalable Yahoo! directory Dmoz Semantic Web Categorization is hard!!:  Categorization is hard!! Multiple concepts activated Choose ONE of the activated concepts. Categorize it! Object worth remembering (article, image…) Analysis-Paralysis! From Rashmi Sinha Tagging is simpler:  Tagging is simpler Multiple concepts are activated Tag it! Note all concepts Object worth remembering (article, image…) From Rashmi Sinha The Personal to the Social:  The Personal to the Social From Rashmi Sinha Tagging is a reality:  Tagging is a reality Bookmarkers tag: Delicious, Rawsugar, Shadows, Simpy, Blinklist, … Bloggers tag: 27 million blogs, doubles every 6 months 1/3rd of blog posts now use tags (or categories) Many more: BBC – news site News - Digg YouTube - Video Flickr, photo publishing and tagging Enterprise? Museums? Cell phones? Most user generated content is tagged ! What Tagging is NOT:  What Tagging is NOT NOT: Generous and altruistic people classifying the Web for the sake of the community NOT: Smart software automatically classifying Web pages and tagging them NOT: A collaborative way to classify the web into a growing giant ontology (folksonomy) So why do People Tag?:  So why do People Tag? Recovery/sharing of personal information: Bookmarks Photos Videos, etc. Increased traffic and findability Bloggers Social reward Advertisement $  Tagging brings value to the tagger Why is Tagging successful?:  Why is Tagging successful? Tagging is free Tagging is easy Tagging brings value [Marlow, Naaman, Boyd & Davis 2006] RawSugar:  RawSugar Covers the last mile of search Provides Guided Search on tagged pages Publish guided search Provide guided search to your site, Blog Get more traffic Receive advertising revenues! Search and Explore Navigate by topics, people, directories Find Experts Slide16:  Nothing to eat here! Slide17:  Still no food here ! Slide18:  Bingo ! What’s Great What’s not Great ? :  What’s Great What’s not Great ? Great: You know what you’re looking for: “Zibibbo restaurant” - Not so great: You’re hungry ! You want to browse - Discover information, explore. You want to know what is popular (“restaurants, digital camera, Java Tutorial, Free Games, etc.”) State of the art: The Last Mile of Search:  State of the art: The Last Mile of Search 83% unhappy with search results (WSJ survey) Most searches point to a list of content websites and directories Navigation of these sites is cumbersome and tedious Google 2 steps approach: Search “restaurants” While (true) { explore guide; } Change the query and Repeat “The last mile of search” Examples: Digital Camera Palo Alto bike Daily Kos Sprol dot Com Where is the last mile?:  Where is the last mile? Google stops here: Human Knowledge: Small and mid-size websites and blogs Content is organized by human and manually: Categorization recommendations Poor search and navigation Each directory is an island of information and does not connect to related directories What’s Missing? Browsing with Facets:  What’s Missing? Browsing with Facets “Easy to discover information without prior knowledge of collection contents “ Faceted Search Paradigm Not new: Library systems: “American history”, “Shakespeare”, etc. Search Engines: Endeca, Shopping.com, Yahoo! Directories, Dmoz, etc. Google/MSN/Yahoo! Local Search - Browse by Location - Current uses: E-Commerce Problems: Maintained by humans – Expensive Rely on a world order – Brittle Facets use a controlled vocabulary – Not easy to define. => Not Scalable Amazon – Faceted Search:  Amazon – Faceted Search Search for Tel Aviv Shopping.com Faceted Search:  Shopping.com Faceted Search Search for Tel Aviv RawSugar Faceted Search:  RawSugar Faceted Search Refine your search RawSugar Faceted Search:  RawSugar Faceted Search Juniorbonner on del.icio.us vs. Juniorbonner on RawSugar RawSugar Into the Last Mile:  RawSugar Into the Last Mile RawSugar inside RawSugar Into the Last Mile:  RawSugar Into the Last Mile RawSugar inside RawSugar Faceted Search in the last mile:  RawSugar Faceted Search in the last mile Daily Kos Blog Search for Iran on RawSugar RawSugar Technology:  RawSugar Technology Problem 1: Searching the TagSpace:  Problem 1: Searching the TagSpace Tags: Ikura, Uni, Ebi, Sushi, Nigiri, Japanese food, lunch in Tokyo, Ezobafun-uni, Kitamurashiuni, Murasakiuni, Akazaebi, Tenagaebi, etc. How would You tag this? How would You search For it? Problem 2: Exploring the TagSpace:  Problem 2: Exploring the TagSpace morphology Locations Restaurant Type Not a restaurant! Problem 3: Exploring the TagSpace:  Problem 3: Exploring the TagSpace Not usable ! RawSugar – Tag Hierarchy Guided Navigation:  RawSugar – Tag Hierarchy Guided Navigation Food groups Locations groups Origins groups RawSugar Tag Hierarchy :  RawSugar Tag Hierarchy Key idea: Some users (4%) define tag hierarchies – (food>sushi, european>spanish, …) We mine this tag space to learn simple tag-relations (ISA relations and RELATED) using statistics. At search time: We apply this learned knowledge to group tags from results. RawSugar –Guided Search Combining Hierarchy Fragments:  RawSugar –Guided Search Combining Hierarchy Fragments europe UK Scotland Edinburgh Spain Italy food vegetarian Sushi food cooking recipes Asian Chinese Thai Southwest California Bay Area San Francisco Texas User 1 User 2 User 3 User4 User 5 RawSugar: Mining and Clustering :  RawSugar: Mining and Clustering Related tags: Tags that are related – (collocations, synonymy, antinomy, ISA, HASA, …) Related pages: Pages tagged similarly Related people: People with similar interests Tags Pages People RawSugar TagSpace sailing Cycling group Related work:  Related work Rashmi Sinha: “Tag Sorting: Another tool in an information architect's toolbox” http://www.rashmisinha.com/archives/05_02/tag-sorting.html Emanuele Quintarelli: “Hierarchical taxonomies from flat tag spaces” http://www.infospaces.it/wordpress/topics/information-architecture/91 Paul Heyman (Stanford): “Tag Hierarchies” http://i.stanford.edu/~heymann/taghierarchy.html Brooks, Montanez, University of San Francisco: “Improved Annotation of the Blogosphere via Autotagging and Hierarchical Clustering ” http://www.cs.usfca.edu/~brooks/papers/brooks-montanez-www06.pdf Siderean fac.etio.us: “Faceted search on delicious tags” http://www.siderean.com/delicious/facetious.jsp Marti Hearst: “Clustering vs. Faceted Search” http://bailando.sims.berkeley.edu/papers/cacm06.pdf And more … Conclusion:  Conclusion Questions? Backup Technology Slides:  Backup Technology Slides What should we do? Smart Backend – Easy Tagging:  What should we do? Smart Backend – Easy Tagging “Tag Relations improve searchability and exploration.” Similar tags: Spelling and morphology: macos<->mac_os<->mac os; tagging <-> tags <->tagged, Synonyms: macos <-> tiger; films <-> movies; new york <-> nyc; Related: cooking <-> recipes, software development <-> programming, Tag groups or subtags: Location -> san francisco, london, new york, etc. Food -> sushi, sashimi, pizza, etc. Programming -> html, java, css, etc. Goal : Discover them by Mining the tag space What should we do? Smart Backend – Friendly Frontend:  What should we do? Smart Backend – Friendly Frontend Backend should not dictate Frontend (Patrick Schmitz, Berkeley/Yahoo!) Smart processing is done by the backend under the hood. Tagging should be as effortless as possible, assisted but not automatic. Fight Analysis-Paralysis (Rashmi Sinha) Systems should be built to incite people to tag. Bring Value to the tagger What is Missing? Tag relations:  What is Missing? Tag relations “Tag Relations improve searchability and exploration.” Similar tags: Spelling and morphology: macos<->mac_os<->mac os; tagging <-> tags <->tagged, Synonyms: macos <-> tiger; films <-> movies; new york <-> nyc; Related: cooking <-> recipes, software development <-> programming, Tag groups or subtags: Location -> san francisco, london, new york, etc. Food -> sushi, sashimi, pizza, etc. Programming -> html, java, css, etc. Goal : Discover them by Mining the tag space Flickr – Clusters:  Flickr – Clusters Clustering – Step 1 Similarity among tags:  Clustering – Step 1 Similarity among tags Some good Clusters found:  Some good Clusters found Tags that belong to the same clusters -:  Tags that belong to the same clusters - Dmoz – World Order:  Dmoz – World Order Dmoz – World Order:  Dmoz – World Order Recommendations: dpreview:  Recommendations: dpreview Faceted Search on TagSpace Challenges:  Faceted Search on TagSpace Challenges Faceted search paradigm on the TagSpace: Not a controlled environment Large scale (1 facet for every 5 documents) Lots of noise: search, search engine, google, search_engines, searchengine, searchengines, search_engine, engine, web, internet, tools, reference, news, information, portal, engines, searching, tech, buscadores, tool … Faceted Search on TagSpace Challenges:  Faceted Search on TagSpace Challenges How to rank facets? What facets should be displayed? How to show them? Performance: Reduce the search space - Refining facets: Tags that allow the user to refine (reduce) the search (depth) Related facets: Tags that allow the user to explore (breadth) Group facets: Cluster tags that are related - Before RawSugar:  Before RawSugar With RawSugar:  With RawSugar navigation Other users Searching the TagSpace with RawSugar: Suggestion Engine :  Searching the TagSpace with RawSugar: Suggestion Engine Goals: Ease of tagging Cohesiveness of our tagspace. Attempts to have our users re-use the same tags instead of creating infinite variations. (search engines, searchengine, search, search tools, search sites, etc.) Key Ideas : Always suggest first the most popular tags Use tag hierarchy and tag context to find the most relevant tags. Use information on the user and the other users to refine the suggestions. What’s Missing? Human Meta Knowledge:  What’s Missing? Human Meta Knowledge Is it good or no? What is it about? Is it popular? Not new: Guides: paloaltoonline.com, expedia.com, etc.. Review Sites - Zagat.com, dpreview.com, etc. Shopping sites – shopping.com, Amazon, Problems: Limited to small environments or verticals (digital camera, restaurants, etc.) Not real search across sites - Manpower – hiring, training, etc. => Not Scalable

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