Design of recommender systems

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

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

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…

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…

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…

Recommender Systems Circa 2001

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)
I know what you will read next summer!

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?

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

Meg & James: correlation = .52
How collaborative filtering algorithms work Recommendations For Meg Lets find a book for Meg!

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
Challenges of Recommender System design

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

Some observations & design principles

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
Source: Sean McNee, John Riedl, Joseph Konstan, CHI Proceedings 2006 “ Making Recommendations Better: An Analytic Model for Human-Recommender Interaction”

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

How to motivate participation
Design principle:
Easy & engaging process for giving input (MediaUnbound)
Ask at the right moment (Netflix)

Give users control…
Design Principle:
Offer filter-like controls for genres/ topics.
Ask how familiar recs should be

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

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
Source: Paolo Massal and Bobby Bhattacharjee, Proc. of 2nd Int. Conference on Trust Management, 2004 “ Using Trust in Recommender Systems: an Experimental Analysis”

Recommendations Circa 2006

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.

Pandora as a textbook example of recommender design principles

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

Last.fm: a social approach to recommendations

Exploring music at Last.fm

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

Other social recommenders…

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”

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

User experiences for finding

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

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

Del.icio.us is useful from saving first link

Design Principle 2: Make system participatory
Bite-sized self-expression
Artistic expression (Flickr, YouTube)
Humor (YouTube)
Beyond rating items – contributions of tags, comments, items
Articles Photos

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”

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

What people are doing on Digg

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?)

Design Principle 5: Cultivate user independence
Prevent mobs, optimize the “wisdom of crowds”

Cultivating wise crowds
Four conditions
Cognitive Diversity
Independence
Decentralization
Easy Aggregation

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

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.

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

Problems of early Recommender Systems addressed
Motivating participation
Giving users fine-grained control
Making item information available
Making recommendations transparent

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

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

Questions?
[email_address]
URLs
www.uzanto.com
www.slideshare.net

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Design of recommender systems

by Rashmi Sinha on Jan 08, 2007

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Design of recommender systems — Presentation Transcript