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Finding Information on the web. Srinivasan Seshadri CTO Kosmix. Early Internet (1992 – 1994). Mozilla Browser People linked to others home pages and other interesting pages People really browsed. INTERNET (1995 – 2002). Search - Altavista, Lycos Google

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Finding Information on the web

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    1. Finding Information on the web Srinivasan Seshadri CTO Kosmix

    2. Early Internet (1992 – 1994) • Mozilla Browser • People linked to others home pages and other interesting pages • People really browsed

    3. INTERNET (1995 – 2002) • Search - Altavista, Lycos • Google • Used Hyperlink Graph Structure to Rank Results

    4. Internet Now • Kosmix bringing back joys of browsing and exploring • 360 degree view of any topic • Topic Home page (why not a topic ) • Top Informational Sites for a topic and a preview (snippets) are the results!

    5. INFORMATION TYPES • Factual Information (Wiki etc.) • Videos • Images • Forum Discussions • Question and Answers • News • Blogs • Structured Information

    6. FUTURE OF SEARCH • First step towards providing multiple pivot points for a topic or search • Need to make this conversational, stateful – like talking to an expert on the topic..

    7. transient Intent and Persistent Intent • TRANSIENT INTENT • Searching for a needle in the haystack • Exploring the haystack for a topic • PERSISTENT INTENT • Interested in the topic for a long time • Carnatic Music, Indian Cricket, Internet Industry, Venture Capital

    8. INFORMATION Deliver information to the consumer what they want when they want how they want where they want

    9. PERSONALIZED NEWSPAPER • My World is Changing • Can not keep track of it • Can my world come to me?

    10. MEDIA INDUSTRY AND INTERNET • Huge pressure on newspapers • Ad spending moving online • More and more content online • Reputed journalists have their own blogs • Content Production; Aggregation and Distribution is becoming disaggregated • Vanilla online newspaper does not exploit what the internet enables • Ability to personalize to nano interests • Publish a personalized newspaper for everyone any time

    11. Key technology Ingredients • Cloud Computing • Categorization • Relevance

    12. Cloud computing at kosmix • Storage: • Biggest Productivity boost in kosmix in the first year • Getting machines to be remotely rebooted! • KFS (Kosmix File System) further lowered the time to make data accessible after machine failures • Computation: • Long Running Computations need to be broken into small restartable/replayable components

    13. Cloud computing at kosmix • Computation Templates: • Most of the computation could be expressed as some variant of a single table scan and some aggregate operation (group by) -- called MapReduce by google • MapReduce not friendly enough to non programmers • SQL not powerful enough in many situations • Need a nice scripting language ..

    14. Opportunity? • Many many companies trying to provide interesting web services • A gold mine of information in the web that can be used by companies • Impractical for each of the companies to build a huge web scale support system (crawling, indexing, KFS, MapReduce etc. etc.) • Further most companies want slivers of the web (typically category based slivers – health forums; travel news sites etc. etc.) • Web and all the derived information is the biggest database perhaps -- can some one make this accessible and easy to use (using some pay you go model) or perhaps some non profit (academia?) angle here?

    15. Categorization • Concept Space: space in which all connections are made within kosmix • Documents, Queries, External Modules, Advertisements, People are all mapped to points in this space and matched.. • Internet Industry, Venture Capital documents need to be mapped to these categories even if they don’t contain the original words

    16. Kategorization at kosmix • Leverage human curated sources • Wiki corpus is a majorr source of knowledge • Huge Automatically Curated Taxonomy • 6 million concepts • Building a Concept Graph with relationship labels where possible • Use a web index to match short pieces of texts with concepts and use taxonomy to refine the matches

    17. Relevance • Need to combine multiple signals into one number to enable ranking • Say Query Relevance Score and Page Relevance Score (text score and page rank) • Signals need to be made comparable • Normalization alone (making ranges the same) is not enough • Need to reconcile different distributions • Deviations from the mean

    18. Relevance • More data always beats smarter algorithms • Adding positions information in the index greatly increases quality • Adding stemming saw a CTR rise of 10% • Adding anchors (and page rank) distinguished google • Adding origin of anchors (hosts) is a much better measure of independent votes • Using demand side popularity (alexa, quantcast) complement web popularity

    19. RELEVANCE • What is a news story? • Cluster news articles.. • Use size of cluster as a measure of popularity • How does one do this efficiently? • Needs to be online since interests/queries are ad hoc • Need to combine some offline preclustering and online methods

    20. summary • Consumer: • Internet has come a long way in terms of getting information to people • Utopian goal of a smart, chatty expert still far away – is a great first step • Need good tools to keep on top of the information explosion – personalized newspaper ( is our first stab at this.. • Technology: • Need to deal with large volume of data • Efficient Data Analysis and Annotation (e.g., Categorization) • Humming Next Gen Database System that grows incrementally, immune to failures, expressive for non programmers