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The Beauty of Computing

… with People

Xavier AmatriainTelefonica Research

November 201010 Years of Computer Science@ Free University of Bolzano

   

Outline

1. Introduction (to the talk, me, and Telefonica)

2. Computing with people: Information overload and Recommender Systems

3. Some of our latest research

4. Conclusions

   

We all know this...

   

But I am here to talk about things in Computer Science

you may not know...

   

CS can be fun

   

And creative...

   

And does not require to be an isolated geek

   

But first...

   

About me

Up until 2005

   

About me

The CLAM Project

   

About me

2005 ­ 2007

   

About me

The Allosphere

   

About me

2007 ­ ..

   

About Telefonica and Telefonica R&D

   

About 71,000 professionals

About 257,000 professionals

Staff

Services

Finances Rev: 4,273 M€

Integrated ICT solutions for all

customers

Clients About 12 million

subscribers

About 265 million

customers

Basic telephone and data services

1989

SpainOperations in 25 countries

Geographies

Rev: 56.7 b€

2000 2009

About 149,000 professionals

About 68 million

customers

Wireline and mobile voice, data and

Internet services

(1) EPS: Earnings per share

Rev: 28,485 M€

Operations in16 countries

Telefonica is a fast-growing Telecom

   

Telco sector worldwide ranking by market cap (US$ bn)

Currently among the largest in the world

Source: Bloomberg, 06/12/09

Telefónica is the sixth worldwide operator in R&D effort and the first company in Spain

17

TELCO OPERATORR&D INVESTMENT 2008 (M€)

NTT 2.151,28

BT 1.157,49

France Telecom 900,00

Telstra 756,41

Telecom Italia 704,00

Telefonica 668,00

Deutsche Telekom 614,00

AT&T 598,57

Vodafone 289,63

KT 218,92

KDDI 155,30

SK Telecom 138,84

Telenor 103,16

TeliaSonera 102,53

COMPANYR&D INVESTMENT 2008 (M€)

Telefonica 668,00

Indra Sistemas 166,34

Almirall 98,20

Repsol YPF 83,00

Iberdrola 73,10

Acciona 71,30

Zeltia 58,09

Fagor Electrodomesticos 56,00

Industria de Turbo Propulsores

50,00

Abengoa 33,54

Gamesa 32,06

Ebro Puleva 11,58

Cie Automotive 11,51

Amper 11,11

   

Scientific Research

Multimedia CoreMobile and Ubicomp

DATA MINING

User Modelling & Data Mining

HCIR

Content Distribution & P2P Wireless Systems

Social Networks

   

Enough introductions already...

Part 2. Information Overload and Recommender Systems

   

Information Overload

   

More is Less

Less Decisions

Worse Decisions

Search engines don’t always hold the answer

What about curiosity?

What about discovery?

What about information to help take decisions?

   

The Age of Search has come to an end

●... long live the Age of Recommendation!● Chris Anderson in “The Long Tail”

● “We are leaving the age of information and entering the age of recommendation”

● CNN Money, “The race to create a 'smart' Google”:● “The Web, they say, is leaving the era of search and entering

one of discovery. What's the difference? Search is what you do when you're looking for something. Discovery is when something wonderful that you didn't know existed, or didn't know how to ask for, finds you.”

   

But, what areRecommender

Systems?

Read This!

Ask Prof. Francesco Ricci

   

The value of recommendations

Netflix: 2/3 of the movies rented are recommended

Google News: recommendations generate 38% more clickthrough

Amazon: 35% sales from recommendations

Choicestream: 28% of the people would buy more music if they found what they liked.

u

   

The “Recommender problem”

● Estimate a utility function that is able to automatically predict how much a user will like an item that is unknown for her. Based on:

● Past behavior● Relations to other users● Item similarity● Context● ...

   

Data mining + all those other things

● User Interface● System requirements (efficiency, scalability,

privacy....)● Business Logic● Serendipity● ....

   

The Netflix Prize

● 500K users x 17K movie titles = 100M ratings = $1M (if you “only” improve existing system by 10%! From 0.95 to 0.85 RMSE)● 49K contestants on 40K teams from

184 countries.

● 41K valid submissions from 5K teams; 64 submissions per day

● Wining approach uses hundreds of predictors from several teams

   

Approaches to Recommendation

●Collaborative Filtering● Recommend items based only on the users past behavior

● User-based● Find similar users to me and recommend what they liked

● Item-based● Find similar items to those that I have previously liked

●Content-based● Recommend based on features inherent to the items

●Social recommendations (trust-based)

   

What works

● It depends on the domain and particular problem● As a general rule, it is usually a good idea to combine:

Hybrid Recommender Systems

● However, in the general case it has been demonstrated that (currently) the best isolated approach is CF.

● Item-based in general more efficient and better but mixing CF approaches can improve result

● Other approaches can be hybridized to improve results in specific cases (cold-start problem...)

35

The CF Ingredients

● List of m Users and a list of n Items● Each user has a list of items with associated opinion

● Explicit opinion - a rating score (numerical scale)● Implicit feedback – purchase records or listening

history● Active user for whom the prediction task is performed● A metric for measuring similarity between users ● A method for selecting a subset of neighbors ● A method for predicting a rating for items not rated by the active user.

But …

Part 3. Some of our latest Research

   

User Feedback is Noisy

DID YOU HEAR WHAT I LIKE??!!

...and limits Our Prediction Accuracy

   

Experimental Setup

● 100 Movies selected from Netflix dataset doing a stratified random sampling on popularity

● Ratings on a 1 to 5 star scale● Special “not seen” symbol.

● Trial 1 and 3 = random order; trial 2 = ordered by popularity

● 118 participants

   

Results

● Users are inconsistent● Inconsistencies are not random and depend on

many factors ● More inconsistencies for mild opinions● More inconsistencies for negative opinions● How the items are presented affects

inconsistencies

● Inconsistencies produce natural noise● Natural noise limits our prediction accuracy

independently of the algorithm: Magic Barrier

   

Rate it again

● By asking users to rate items again we can remove noise in the dataset● Improvements of up to 14% in accuracy!

● Because we don't want all users to re-rate all items we design ways to do partial denoising● Data-dependent: only denoise extreme ratings● User-dependent: detect “noisy” users

   

Who Can we trust?

The Wisdom of the Few

X. Amatriain et al. "The wisdom of the few: a collaborative filtering approach based on expert opinions from the web", SIGIR '09

   

Expert-based CF

● expert = individual that we can trust to have produced thoughtful, consistent and reliable evaluations (ratings) of items in a given domain

● Expert-based Collaborative Filtering● Find neighbors from a reduced set of experts instead of

regular users.

1. Identify domain experts with reliable ratings

2. For each user, compute “expert neighbors”

3. Compute recommendations similar to standard kNN CF

   

Working Prototypes

Music recommendations, mobile geo-located recommendations...

   

User Study

● 57 participants, only 14.5 ratings/participant

● 50% of the users consider Expert-based CF to be good or very good

● Expert-based CF: only algorithm with an average rating over 3 (on a 0-4 scale)

Context Overload

≠

Mobile phones are “personal”

Where is the nearest florist?

Where is that really cool cocktail barI went to last month?

Interesting things close to me?

Events near me?

   

Context-aware Recommendations

● A clear area of research and interest for companies: recommend me something that I like and is relevant in my current context.● Context = any variable that adds a new dimension

to the 2D user-item problem (e.g. time, geolocation, weather...)

   

User micro-profiles

● Our proposal is to represent a user by a hierarchy of micro-profiles where each micro-profile represents a class in the context variable

   

Multiverse Recommendation

● A different approach: represent the contextual recommendation problem by n-dimensional matrices (aka Tensors)

   

Master Planner

Automatic and personalized tourist route recommendations, a new approach to discovering the world

   

Tourism 2.0

● Tourism is not the same since the web appeared:– People search for 

information on where to go online (reading blogs, in their social networks...)

– People buy tickets and hotel packages online

– People post pictures and discuss tips online

   

Tourism 3.0 – Going Mobile

● The mobile web and smartphones are introducing yet another revolution

– Tourists can now access information on the go:● Looking for information on a sight● Tips on where to go next● Information about the weather● ....

   

Master Planner

● I am in Bolzano, it's November and sunny, I have 6 hours to visit things and I am interested on music, art, literature, and sports

● I need: An automatic tourist route recommender system

   

Master Planner

● Completely automatic personalized/contextualized tourist recommender system

● Generates automatic city models using web resources

● Generates automatic user models from regular user profiles

● Personalizes/contextualizes generic city models

● Recommends optimized personalized routes taking into account constraints using AI techniques

   

Friending 3.0

Recommending contacts in Social Networks

   

The importance of finding contacts

● The ability to attract people to a social network is the key to its success

● The main reason people get hooked to a particular SN is because they find relevant “friends”

   

The concept of “friend”

● The idea of “friend” is different for each SN– People do not connect on Facebook for the 

same reasons than in Twitter or Linkedin

● Even in a particular SN, different people connect for different reasons:

– Social proximity (friend of friend)

– Geo­proximity (person who lives nearby)

– Content (person that talks about interesting stuff)

– Popularity (to connect to influential people)

– ....

   

Friending 3.0

● Automatic Personalized Friend Recommending System

● Basic rationale– Combines different factors 

and personalizes the combination for each user:

● Social proximity● Geo­proximity● Popularity● Content similarity● ...

But friends are not only for fun...

They can be very helpful sometimes!

Adriana

Catalan

Can we improve the search and discovery experience by providing a readily available connection to

their social network?

WHAT IS PORQPINE?

● Distributed social web search engine● Locally caches the page & records user

interactions (e.g., bookmarking). ● Searches by querying caches of friends

● Pages that friends have “interacted with” are ranked higher

Personalized

Distrib

uted

Lazy collaboration

Socially aware

Contex

t­awar

e

SSB

iPhone optimized web-application + Facebook app

When launched it centers on the users current physical location

Displays all queries/questions posted by other users in that location

As users pan/zoom the set of queries is updated

Users can post new queries or interact with queries of others

Live Field Study in-the-wild

Apr 2009, 16 users, 1 week, ireland

Sept 2009, 34 users, 1 month, ireland

   

Part 4. Conclusions

   

Conclusions

● Computer Science is not only a good choice from a career perspective, it's also fun, creative, and engaging (Hope I have convinced you by now)

● One of the amazing things is that you can now apply CS research to any domain (I am meeting the world's best chef next week to brainstorm)

● An important current trend is to use CS to better understand people and improve their lives

● The goal of Recommender Systems is precisely that: understand you in your context and help you take better decisions

   

Thanks!

Questions?

Xavier Amatriainxar@tid.es

http://xavier.amatriain.nethttp://technocalifornia.blogspot.com

http://twitter.com/xamat