Design of recommender systems

Slide 1: Design Strategies for Recommender Systems Rashmi Sinha www.uzanto.com Jan 2006, UIE Web App Summit

Slide 2: What are Recommender Systems? Circa 2001  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) e.g., Amazon – people who liked this book also liked, Netflix  recommendations Circa 2006  Systems that help people find information that  will interest them, by facilitating social / conceptual connections or other means… Pandora, Last.fm  2 UIE Web App Summit

Slide 3: Designing different finding experiences Some experiences guide user, others just point  in a general direction Desired experience depends on user task, time  constraints, mood etc. There’s more than one way to get from here to there… 3 UIE Web App Summit

Slide 4: User experience in search/browse interfaces More controlled  experience Every movement  (forward, making a turn) is a conscious choice System should provide  information at every step If user takes wrong turn,  go back a step or two / start again Like driving a car… 4 UIE Web App Summit

Slide 5: User Experience with Recommender Systems User has less control over  specifics of interaction System does not provide  information about specifics of action More of a “black box” model  (some input from user, output from systems) Like riding a roller coaster… 5 UIE Web App Summit

Slide 6: Recommender Systems Circa 2001

Slide 7: I know what you will read next summer! what movies you should watch…  (Reel, RatingZone, Amazon) what music you should listen to…  (CDNow, Mubu, Gigabeat) what websites you should visit…  (Alexa) what jokes you will like… (Jester)  where to go on vacation (TripleHop)  & who you should date… (Yenta)  7 UIE Web App Summit

Slide 8: A technological proxy for a social process Friends / Family What should I read next? Ref: Flickr-BlueAlgae “I think you would enjoy reading these books…” Ref: Flickr photostream: jefield Ref: Flickr-Lady_Strathconn 8 UIE Web App Summit

Slide 9: Interaction paradigm Input: Rate some books What should I read next? Output: “Books you might enjoy are…” 9 Ref Flickr photostreams: anjill154 & rossination UIE Web App Summit

Slide 10: How collaborative filtering algorithms work Lets find a book for Meg! Ratings of Books 1 2 3 4 5 6 7 8 Meg 5 3 3 4 2 1 Jim 3 4 2 3 4 5 1 3 Nick 4 3 1 2 4 2 4 1 James 4 2 1 3 4 1 5 5 Recommendations Meg & James: correlation =  .52 For Meg 10 UIE Web App Summit

Slide 11: Challenges of Recommender System design Input: Motivating users to give input (to feed  collaborative filtering algorithms) System: Making good, useful recommendations  (effectiveness of algorithm) Output (Recommendations):  Presenting recommendations quickly enough but not  too quickly (knowing when to say “I can’t recommend”)  Generating trust that system understands user tastes  Providing enough information about each item 11 UIE Web App Summit

Slide 12: Domain differences drive design Form of sample (song clip vs. product  description vs. full text article) Genres: how fixed and predictable are they?  Frequency of updates (e.g., news & other fast-  flowing content) Commerce vs. taste exploration vs. info-  seeking 12 UIE Web App Summit

Slide 13: Some observations & design principles

Slide 14: Trust is crucial Users think recommender systems have  personalities First impressions are crucial  Does system understand me?   Should I act on its recommendations? Two different approaches:  Amazon offers affirming experience: familiar items may be  correct but not as useful (not new information)  MediaUnbound: less familiar, so more salient and possibly serendipitous, but less likely be acted upon 14 Source: Sean McNee, John Riedl, Joseph Konstan, CHI Proceedings 2006 “Making Recommendations Better: An Analytic Model for Human-Recommender Interaction” UIE Web App Summit

Slide 15: Make system logic transparent Users want to understand why an item  was recommended to them To decide whether to accept  recommendation Explaining recommendations  Identify the input for particular  recommendation 15 UIE Web App Summit

Slide 16: How to motivate participation Design principle:  Easy & engaging  process for giving input (MediaUnbound) Ask at the right moment (Netflix) 16 UIE Web App Summit

Slide 17: Give users control… Design Principle:  Offer filter-like  controls for genres/ topics.  Ask how familiar recs should be 17 UIE Web App Summit

Slide 18: Provide detailed info about recommended items Design principle: Provide clear paths to detailed  item information and community feedback such as Reviews  Ratings by other users  Sample of item  18 UIE Web App Summit

Slide 19: The unfulfilled promise of Recommender Systems Some very popular systems (Amazon & Netflix)  Overall, recommender systems lost steam—  nowhere near as popular as search. Data sparseness (unlike search which builds on  preexisting data – hyperlinks) Cold start problem  Interface issues  Gaming the system / spam etc.  Hard to understand and control  Lacked a larger purpose; an end in themselves  19 Source: Paolo Massal and Bobby Bhattacharjee, Proc. of 2nd Int. Conference on Trust Management, 2004 “Using Trust in Recommender Systems: an Experimental Analysis” UIE Web App Summit

Slide 20: Recommendations Circa 2006

Slide 21: What’s happened in the interim? Social networking systems (Friendster, Orkut,  LinkedIn, MySpace) Blogs, Wikis  Tagging / folksonomies  Google AdSense  YouTube  Rich interfaces (AJAX / Flash)  People read, write, play, share pics, videos on  the web. They live their lives on the web. 21 UIE Web App Summit

Slide 22: Pandora as a textbook example of recommender design principles 22 UIE Web App Summit

Slide 23: Characteristics of Pandora Rich interface makes experience seamless  Starts giving results with one click  Puts user in control of recommendation  Takes a conversational tone  Transparent logic  Generates trust  Problems  Not scalable approach   Not social approach: feels like a machine doing thinking for me 23 UIE Web App Summit

Slide 24: Last.fm: a social approach to recommendations 24 UIE Web App Summit

Slide 25: Exploring music at Last.fm 25 UIE Web App Summit

Slide 26: Characteristics of Last.fm Quick start, friendly interface  Multiple points of entry: charts,  tags, users, new items - not just what system recommends for you Focus on social approach  Listen to other users’ radio stations  (Friends, Neighbors, Groups) Read journals  Chat on message boards  Highlights contributions to system:  your radio station is available to others 26 UIE Web App Summit

Slide 27: Other social recommenders… 27 UIE Web App Summit

Slide 28: What do these systems have in common? User-generated content: mass participation &  social sharing User-curated content: tags, collections etc.  Harnessing wisdom of crowds  Granular addressability of content  The long tail: making the esoteric more findable  Incorporating social networks  Rich user experience  Not all work: elements of fun and play  Tim O’Reilly, “What is Web 2.0: Design Patterns and Business Models for the Next Generation of Software” 28 UIE Web App Summit

Slide 29: A revolution in RS user experience 2001 2006 Information & Social Intelligent Hubs Agents • User interacts with other users, • User interacts with algorithm to get their content and tags to find recommendations information & connect with people • System may use aggregated data • Frequently tag-based about other users (via collaborative • Data from other users is exposed filtering algorithms). That data is not and updated in real-time directly accessible to all • Succeeds by building a social web, • Centered on completing a finding making it more like an ongoing task or making sales conversation than a transaction 29 UIE Web App Summit

Slide 30: User experiences for finding 30 UIE Web App Summit

Slide 31: User experience with social recommender systems Move at a slower pace  Get the lay of the land,  experience surroundings  Choose paths – what is  promising, what sights lie on the way, how well worn. Easy to change directions,  change paths, create your own path Flickr photostream: soundfromwayout 31 UIE Web App Summit

Slide 32: Design Principle 1: Make system personally useful (before recommendations) System should serve other useful purpose  before it starts personalizing Portable storage (photos, bookmarks)  Aggregate popular news stories & feeds  Offer vehicle for trendsetters / trendspotters  Provide a discussion forum  Personalize once system has user data  Solves input problem of early RS  32 UIE Web App Summit

Slide 33: Del.icio.us is useful from saving first link 33 UIE Web App Summit

Slide 34: Design Principle 2: Make system participatory Bite-sized self-expression  Artistic expression (Flickr, YouTube)  Humor (YouTube)  Beyond rating items – contributions of tags, comments,  items Photos Articles 34 UIE Web App Summit

Slide 35: Different types of participation Social software sites don’t require 100% active  participation to generate great value. Implicit creation (creating by consuming)  Remixing—adding value to others’ content  Source: Bradley Horowitz’s weblog, Elatable, Feb. 17, 2006, “Creators, Synthesizers, and Consumers” 35 UIE Web App Summit

Slide 36: Design Principle 3: Make participatory process social Real-time updating makes it feel more like a  conversation; sense that others are out there User profiles and photos put a human face on  the system interactions Spotback 36 UIE Web App Summit

Slide 37: What people are doing on Digg 37 UIE Web App Summit

Slide 38: Design Principle 4: Instant gratification Provide personalized recommendations as soon as a  user provides some input Pandora: one song  instant radio station  Spotback: one article rating  instant articles of interest  Note: need lots of user data for this to work well (cold  start problem emerges again?) 38 UIE Web App Summit

Slide 39: Design Principle 5: Cultivate user independence Prevent mobs, optimize the “wisdom of  crowds” 39 UIE Web App Summit

Slide 40: Cultivating wise crowds Four conditions • Cognitive Diversity • Independence • Decentralization • Easy Aggregation 40 UIE Web App Summit

Slide 41: Design Principle 6: Provide access to long tail, keep content fast moving Make “long tail” accessible  Recommend lots of different stuff (not just most  popular) Top 100 lists  Keeps recs from getting stale  Use time as a dimension in system design  Enable fast movement. Rise to top. Get  displaced. e.g., “what’s fresh today” e.g., Slideshare popularity model  41 UIE Web App Summit

Slide 42: Design Principle 7: Expose metadata, make it linkable Exposing tags and user lists  Enable “pivot browsing”  Every piece of content should have a unique, easily  guessed URL. 42 UIE Web App Summit

Slide 43: Design Principle 8: Provide balance between public & private People can be willing to share a lot if they get  the right returns Allow users to:  Filter by topic/category  Indicate “more like this” and “no more like this”  Delete items from reading history or reset profile  completely Privacy settings on Flickr 43 UIE Web App Summit

Slide 44: Problems of early Recommender Systems addressed Motivating participation  Giving users fine-grained control  Making item information available  Making recommendations transparent  44 UIE Web App Summit

Slide 45: So what’s left to solve? Possible problems:  Mob rule (ends up recommending “lowest  common denominator items”) Trust issues: why should I trust another  user, or the community as a whole? Degree of serendipity to allow; methods  for adjusting this setting 45 UIE Web App Summit

Slide 46: Things to try at home! Create an account on myspace.com  Read Emergence, Wisdom of Crowds  Play a Multiplayer Online Game (WOW, Second  Life) Play with an API (try GoogleMaps API)  Try a mobile social application (DodgeBall)  Ask your friends what they find “fun” on the web  46 UIE Web App Summit

Slide 47: Questions? rashmi@uzanto.com URLs www.uzanto.com www.slideshare.net 47 UIE Web App Summit