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

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…

Some experiences guide user, others just point in a general direction

Desired experience depends on user task, time constraints, mood etc.

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…

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

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…

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)

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!

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)

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!

Meg & James: correlation = .52

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

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

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

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”

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

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

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)

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

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

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”

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

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.

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

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

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”

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

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 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

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

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

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

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

Bite-sized self-expression

Artistic expression (Flickr, YouTube)

Humor (YouTube)

Beyond rating items – contributions of tags, comments, items

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”

Social software sites don’t require 100% active participation to generate great value.

Implicit creation (creating by consuming)

Remixing—adding value to others’ content

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

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

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?)

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”

Prevent mobs, optimize the “wisdom of crowds”

Cultivating wise crowds Four conditions Cognitive Diversity Independence Decentralization Easy Aggregation

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

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.

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

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

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

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

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

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

Questions?

[email_address]

URLs

www.uzanto.com

www.slideshare.net