Design of recommender systems

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Design of recommender systems

  1. 1. Design Strategies for Recommender Systems Rashmi Sinha www.uzanto.com Jan 2006, UIE Web App Summit
  2. 2. What are Recommender Systems? <ul><li>Circa 2001 </li></ul><ul><ul><li>Systems that attempt to predict items, e.g., movies, music, books, that a user may be interested in (given some information about the user's profile) </li></ul></ul><ul><ul><ul><li>e.g., Amazon – people who liked this book also liked, Netflix recommendations </li></ul></ul></ul><ul><li>Circa 2006 </li></ul><ul><ul><li>Systems that help people find information that will interest them, by facilitating social / conceptual connections or other means… </li></ul></ul><ul><ul><ul><li>Pandora, Last.fm </li></ul></ul></ul>
  3. 3. Designing different finding experiences <ul><li>Some experiences guide user, others just point in a general direction </li></ul><ul><li>Desired experience depends on user task, time constraints, mood etc. </li></ul>There’s more than one way to get from here to there…
  4. 4. User experience in search/browse interfaces <ul><li>More controlled experience </li></ul><ul><li>Every movement (forward, making a turn) is a conscious choice </li></ul><ul><ul><li>System should provide information at every step </li></ul></ul><ul><li>If user takes wrong turn, go back a step or two / start again </li></ul>Like driving a car…
  5. 5. User Experience with Recommender Systems <ul><li>User has less control over specifics of interaction </li></ul><ul><li>System does not provide information about specifics of action </li></ul><ul><li>More of a “black box” model (some input from user, output from systems) </li></ul>Like riding a roller coaster…
  6. 6. Recommender Systems Circa 2001
  7. 7. <ul><li>what movies you should watch… (Reel, RatingZone, Amazon) </li></ul><ul><li>what music you should listen to… (CDNow, Mubu, Gigabeat) </li></ul><ul><li>what websites you should visit… (Alexa) </li></ul><ul><li>what jokes you will like… (Jester) </li></ul><ul><li>where to go on vacation (TripleHop) </li></ul><ul><li>& who you should date… (Yenta) </li></ul>I know what you will read next summer!
  8. 8. A technological proxy for a social process “ I think you would enjoy reading these books…” Friends / Family Ref: Flickr photostream: jefield Ref: Flickr-BlueAlgae Ref: Flickr-Lady_Strathconn What should I read next?
  9. 9. Interaction paradigm “ Books you might enjoy are…” Output: Input: Rate some books Ref Flickr photostreams: anjill154 & rossination What should I read next?
  10. 10. <ul><li>Meg & James: correlation = .52 </li></ul>How collaborative filtering algorithms work Recommendations For Meg Lets find a book for Meg!
  11. 11. <ul><li>Input : Motivating users to give input (to feed collaborative filtering algorithms) </li></ul><ul><li>System : Making good, useful recommendations (effectiveness of algorithm) </li></ul><ul><li>Output (Recommendations) : </li></ul><ul><ul><li>Presenting recommendations quickly enough but not too quickly (knowing when to say “I can’t recommend”) </li></ul></ul><ul><ul><li>Generating trust that system understands user tastes </li></ul></ul><ul><ul><li>Providing enough information about each item </li></ul></ul>Challenges of Recommender System design
  12. 12. Domain differences drive design <ul><li>Form of sample (song clip vs. product description vs. full text article) </li></ul><ul><li>Genres: how fixed and predictable are they? </li></ul><ul><li>Frequency of updates (e.g., news & other fast-flowing content) </li></ul><ul><li>Commerce vs. taste exploration vs. info-seeking </li></ul>
  13. 13. Some observations & design principles
  14. 14. Trust is crucial <ul><li>Users think recommender systems have personalities </li></ul><ul><li>First impressions are crucial </li></ul><ul><ul><li>Does system understand me? </li></ul></ul><ul><ul><li>Should I act on its recommendations? </li></ul></ul><ul><li>Two different approaches: </li></ul><ul><ul><li>Amazon offers affirming experience: familiar items may be correct but not as useful (not new information) </li></ul></ul><ul><ul><li>MediaUnbound : less familiar, so more salient and possibly serendipitous, but less likely be acted upon </li></ul></ul>Source: Sean McNee, John Riedl, Joseph Konstan, CHI Proceedings 2006 “ Making Recommendations Better: An Analytic Model for Human-Recommender Interaction”
  15. 15. Make system logic transparent <ul><li>Users want to understand why an item was recommended to them </li></ul><ul><ul><li>To decide whether to accept recommendation </li></ul></ul><ul><ul><li>Explaining recommendations </li></ul></ul><ul><ul><ul><li>Identify the input for particular recommendation </li></ul></ul></ul>
  16. 16. How to motivate participation <ul><li>Design principle: </li></ul><ul><ul><li>Easy & engaging process for giving input (MediaUnbound) </li></ul></ul><ul><ul><li>Ask at the right moment (Netflix) </li></ul></ul>
  17. 17. Give users control… <ul><li>Design Principle: </li></ul><ul><ul><li>Offer filter-like controls for genres/ topics. </li></ul></ul><ul><ul><li>Ask how familiar recs should be </li></ul></ul>
  18. 18. Provide detailed info about recommended items <ul><li>Design principle: Provide clear paths to detailed item information and community feedback such as </li></ul><ul><ul><li>Reviews </li></ul></ul><ul><ul><li>Ratings by other users </li></ul></ul><ul><ul><li>Sample of item </li></ul></ul>
  19. 19. The unfulfilled promise of Recommender Systems <ul><li>Some very popular systems (Amazon & Netflix) </li></ul><ul><li>Overall, recommender systems lost steam—nowhere near as popular as search. </li></ul><ul><ul><li>Data sparseness (unlike search which builds on preexisting data – hyperlinks) </li></ul></ul><ul><ul><li>Cold start problem </li></ul></ul><ul><ul><li>Interface issues </li></ul></ul><ul><ul><li>Gaming the system / spam etc. </li></ul></ul><ul><ul><li>Hard to understand and control </li></ul></ul><ul><ul><li>Lacked a larger purpose; an end in themselves </li></ul></ul>Source: Paolo Massal and Bobby Bhattacharjee, Proc. of 2nd Int. Conference on Trust Management, 2004 “ Using Trust in Recommender Systems: an Experimental Analysis”
  20. 20. Recommendations Circa 2006
  21. 21. What’s happened in the interim? <ul><li>Social networking systems (Friendster, Orkut, LinkedIn, MySpace) </li></ul><ul><li>Blogs, Wikis </li></ul><ul><li>Tagging / folksonomies </li></ul><ul><li>Google AdSense </li></ul><ul><li>YouTube </li></ul><ul><li>Rich interfaces (AJAX / Flash) </li></ul><ul><li>People read, write, play, share pics, videos on the web. They live their lives on the web. </li></ul>
  22. 22. Pandora as a textbook example of recommender design principles
  23. 23. Characteristics of Pandora <ul><li>Rich interface makes experience seamless </li></ul><ul><li>Starts giving results with one click </li></ul><ul><li>Puts user in control of recommendation </li></ul><ul><li>Takes a conversational tone </li></ul><ul><li>Transparent logic </li></ul><ul><li>Generates trust </li></ul><ul><li>Problems </li></ul><ul><ul><li>Not scalable approach </li></ul></ul><ul><ul><li>Not social approach: feels like a machine doing thinking for me </li></ul></ul>
  24. 24. Last.fm: a social approach to recommendations
  25. 25. Exploring music at Last.fm
  26. 26. Characteristics of Last.fm <ul><li>Quick start, friendly interface </li></ul><ul><li>Multiple points of entry: charts, tags, users, new items - not just what system recommends for you </li></ul><ul><li>Focus on social approach </li></ul><ul><ul><li>Listen to other users’ radio stations (Friends, Neighbors, Groups) </li></ul></ul><ul><ul><li>Read journals </li></ul></ul><ul><ul><li>Chat on message boards </li></ul></ul><ul><li>Highlights contributions to system: your radio station is available to others </li></ul>
  27. 27. Other social recommenders…
  28. 28. What do these systems have in common? <ul><li>User-generated content: mass participation & social sharing </li></ul><ul><ul><li>User- curated content: tags, collections etc. </li></ul></ul><ul><ul><li>Harnessing wisdom of crowds </li></ul></ul><ul><li>Granular addressability of content </li></ul><ul><li>The long tail: making the esoteric more findable </li></ul><ul><li>Incorporating social networks </li></ul><ul><li>Rich user experience </li></ul><ul><li>Not all work: elements of fun and play </li></ul>Tim O’Reilly, “What is Web 2.0: Design Patterns and Business Models for the Next Generation of Software”
  29. 29. A revolution in RS user experience <ul><li>User interacts with algorithm to get recommendations </li></ul><ul><li>System may use aggregated data about other users (via collaborative filtering algorithms). That data is not directly accessible to all </li></ul><ul><li>Centered on completing a finding task or making sales </li></ul><ul><li>User interacts with other users, their content and tags to find information & connect with people </li></ul><ul><li>Frequently tag-based </li></ul><ul><li>Data from other users is exposed and updated in real-time </li></ul><ul><li>Succeeds by building a social web, making it more like an ongoing conversation than a transaction </li></ul>2001 2006 Intelligent Agents Information & Social Hubs
  30. 30. User experiences for finding
  31. 31. User experience with social recommender systems <ul><li>Move at a slower pace </li></ul><ul><li>Get the lay of the land, </li></ul><ul><ul><li>experience surroundings </li></ul></ul><ul><li>Choose paths – what is promising, what sights lie on the way, how well worn. </li></ul><ul><li>Easy to change directions, change paths, create your own path </li></ul>Flickr photostream: soundfromwayout
  32. 32. Design Principle 1: Make system personally useful (before recommendations) <ul><li>System should serve other useful purpose before it starts personalizing </li></ul><ul><ul><li>Portable storage (photos, bookmarks) </li></ul></ul><ul><ul><li>Aggregate popular news stories & feeds </li></ul></ul><ul><ul><li>Offer vehicle for trendsetters / trendspotters </li></ul></ul><ul><ul><li>Provide a discussion forum </li></ul></ul><ul><li>Personalize once system has user data </li></ul><ul><ul><li>Solves input problem of early RS </li></ul></ul>
  33. 33. Del.icio.us is useful from saving first link
  34. 34. Design Principle 2: Make system participatory <ul><li>Bite-sized self-expression </li></ul><ul><ul><li>Artistic expression (Flickr, YouTube) </li></ul></ul><ul><ul><li>Humor (YouTube) </li></ul></ul><ul><li>Beyond rating items – contributions of tags, comments, items </li></ul>Articles Photos
  35. 35. Different types of participation <ul><li>Social software sites don’t require 100% active participation to generate great value. </li></ul><ul><ul><li>Implicit creation (creating by consuming) </li></ul></ul><ul><ul><li>Remixing—adding value to others’ content </li></ul></ul>Source: Bradley Horowitz’s weblog, Elatable, Feb. 17, 2006, “Creators, Synthesizers, and Consumers”
  36. 36. Design Principle 3: Make participatory process social <ul><li>Real-time updating makes it feel more like a conversation; sense that others are out there </li></ul><ul><li>User profiles and photos put a human face on the system interactions </li></ul>Spotback
  37. 37. What people are doing on Digg
  38. 38. Design Principle 4: Instant gratification <ul><li>Provide personalized recommendations as soon as a user provides some input </li></ul><ul><ul><li>Pandora: one song  instant radio station </li></ul></ul><ul><ul><li>Spotback: one article rating  instant articles of interest </li></ul></ul><ul><li>Note: need lots of user data for this to work well (cold start problem emerges again?) </li></ul>
  39. 39. Design Principle 5: Cultivate user independence <ul><li>Prevent mobs, optimize the “wisdom of crowds” </li></ul>
  40. 40. Cultivating wise crowds <ul><li>Four conditions </li></ul><ul><li>Cognitive Diversity </li></ul><ul><li>Independence </li></ul><ul><li>Decentralization </li></ul><ul><li>Easy Aggregation </li></ul>
  41. 41. Design Principle 6: Provide access to long tail, keep content fast moving <ul><li>Make “long tail” accessible </li></ul><ul><ul><li>Recommend lots of different stuff (not just most popular) </li></ul></ul><ul><ul><ul><li>Top 100 lists </li></ul></ul></ul><ul><ul><li>Keeps recs from getting stale </li></ul></ul><ul><li>Use time as a dimension in system design </li></ul><ul><ul><li>Enable fast movement. Rise to top. Get displaced. e.g., “what’s fresh today” </li></ul></ul><ul><ul><ul><li>e.g., Slideshare popularity model </li></ul></ul></ul>
  42. 42. Design Principle 7: Expose metadata, make it linkable <ul><li>Exposing tags and user lists </li></ul><ul><ul><li>Enable “pivot browsing” </li></ul></ul><ul><li>Every piece of content should have a unique, easily guessed URL. </li></ul>
  43. 43. Design Principle 8: Provide balance between public & private <ul><li>People can be willing to share a lot if they get the right returns </li></ul><ul><li>Allow users to: </li></ul><ul><ul><li>Filter by topic/category </li></ul></ul><ul><ul><li>Indicate “more like this” and “no more like this” </li></ul></ul><ul><ul><li>Delete items from reading history or reset profile completely </li></ul></ul>Privacy settings on Flickr
  44. 44. Problems of early Recommender Systems addressed <ul><li>Motivating participation </li></ul><ul><li>Giving users fine-grained control </li></ul><ul><li>Making item information available </li></ul><ul><li>Making recommendations transparent </li></ul>
  45. 45. So what’s left to solve? <ul><li>Possible problems: </li></ul><ul><ul><li>Mob rule (ends up recommending “lowest common denominator items”) </li></ul></ul><ul><ul><li>Trust issues: why should I trust another user, or the community as a whole? </li></ul></ul><ul><ul><li>Degree of serendipity to allow; methods for adjusting this setting </li></ul></ul>
  46. 46. Things to try at home! <ul><li>Create an account on myspace.com </li></ul><ul><li>Read Emergence, Wisdom of Crowds </li></ul><ul><li>Play a Multiplayer Online Game (WOW, Second Life) </li></ul><ul><li>Play with an API (try GoogleMaps API) </li></ul><ul><li>Try a mobile social application (DodgeBall) </li></ul><ul><li>Ask your friends what they find “fun” on the web </li></ul>
  47. 47. <ul><li>Questions? </li></ul><ul><li>[email_address] </li></ul><ul><li>URLs </li></ul><ul><li>www.uzanto.com </li></ul><ul><li>www.slideshare.net </li></ul>

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