Media, data, context... and the Holy Grail of User Taste Prediction

MEDIA, DATA, CONTEXT... And The Holy Grail of User Taste Prediction

Xavier Amatriain

MAT, UCSBSanta Barbara, March '11

But first...

About me and Telefonica

About meUp until 2005

About me2005 ­ 2007

About me2007 ­ ..

About 71,000 professionals

About 257,000 professionals

Staff

Services

Finances Rev: 4,273 M€EPS(1): 0.45 €

Integrated ICT solutions for all

customers

Clients About 12 million

subscribers

About 260 million

customers

Basic telephone and data services

1989

SpainOperations in 25 countries

Geographies

Rev: 57,946 M€ EPS: 1.63 €

2000 2008

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€EPS(1): 0.67 €

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

Just announced 2010 results: record net earnings, first Spanish company ever to make > 10B €

Argentina: 20.9 millionBrazil: 61.4 millionCentral America: 6.1 millionColombia: 12.6 millionChile: 10.1 millionEcuador: 3.3 million Mexico: 15.7 millionPeru: 15.2 millionUruguay: 1.5 millionVenezuela: 12.0 million

Wireline market rank Mobile market rank

21

12

21

11

2

2

11

12

2

Notes: - Central America includes Guatemala, Panama, El Salvador and Nicaragua- Total accesses figure includes Narrowband Internet accesses of Terra Brasil and Terra Colombia, and Broadband Internet accesses of Terra Brasil, Telefónica de Argentina, Terra Guatemala and Terra México.

Data as of March ‘09

Total Accesses (as of March ‘09)159.5 million

Leader in South America

Spain: 47.2 millionUK: 20.8 millionGermany: 16.0 millionIreland: 1.7 millionCzech Republic: 7.7 millionSlovakia: 0.4 million

Total Accesses (as of March ’09)93.8 million

1

21

11

4

2

Wireline market rankMobile market rank

3

Data as of March ‘09

And a significant footprint in Europe

Scientific Research

Multimedia CoreMobile and Ubicomp

DATA MINING

User Modelling & Data Mining

HCIR

Content Distribution & P2P Wireless Systems

Social Networks

Enough introductions...

Information Overload

More is Less

Less Decisions

Worse Decisions

Search engines don’t always hold the answer

What about discovery?

What about curiosity?

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!

Attend this conference!

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...)

27

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 ...

User Feedback is Noisy

DID YOU HEAR WHAT I LIKE??!!

...and limits Our Prediction Accuracy

The Magic Barrier

● Magic Barrier = Limit on prediction accuracy due to noise in original data

● Natural Noise = involuntary noise introduced by users when giving feedback● Due to (a) mistakes, and (b) lack of resolution in

personal rating scale (e.g. In a 1 to 5 scale a 2 may mean the

same than a 3 for some users and some items).

● Magic Barrier >= Natural Noise Threshold● We cannot predict with less error than the

resolution in the original data

Our related research questions

● Q1. Are users inconsistent when providing explicit feedback to Recommender Systems via the common Rating procedure?

● Q2. How large is the prediction error due to these inconsistencies?

● Q3. What factors affect user inconsistencies?

X. Amatriain, J.M. Pujol, N. Oliver (2009) "I like It... I like It Not: Measuring Users Ratings Noise in Recommender Systems", in UMAP 09

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

User Feedback is Noisy

● 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

User’s ratings are far from ground truth

Pairwise comparison between trials, RMSE is already > 0.55 or > 0.69 in the best case (Netflix Prize was to get below 0.85 !!!)

Rate it Again

● Given that users are noisy… can we benefit from asking to rate the same movie more than once?

● We propose an algorithm to allow for multiple ratings of the same <user,item> tuple.● The algorithm is subjected to two fairness conditions:

– Algorithm should remove as few ratings as possible (i.e. only when there is some certainty that the rating is only adding noise)

– Algorithm should not make up new ratings but decide on which of the existing ones are valid (no averaging, predicting...)

X. Amatriain, J.M. Pujol, N. Tintarev, N. Oliver (2009)"Rate it Again: Increasing Recommendation Accuracy by User re-Rating", 2009 ACM RecSys

Re-rating Algorithm• One source re­rating case:

• Given the following milding function:   

Examples:

{3, 1} → Ø {4} → 4{3, 4} → 3

(2 source){3, 4, 5} → 3

Results

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

The value or a re-rating

Adding new ratings increases performance of the CF algorithm

The value or a re-rating

But you are better off doing re-rating than new ratings !!

The value or a re-rating

And much better if you know which ratings to re-rate!!

Let's recap

● Users are inconsistent● Inconsistencies can depend on many things

including how the items are presented● Inconsistencies produce natural noise● Natural noise reduces our prediction accuracy

independently of the algorithm● By asking users to rate items again we can

remove noise and improve accuracy

But Crowds are not always wise

● Diversity of opinion

● Independence

● Decentralization

● Aggregation

Conditions that are needed to guarantee the Wisdom in a Crowd

Who Can we trust?

Crowds are not always wise

vs.

Who  won?

“It is really only experts who can reliably account 

for their reactions”

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

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)

Advantages of the Approach

● Noise● Experts introduce less

natural noise

● Malicious Ratings● Dataset can be monitored

to avoid shilling

● Data Sparsity● Reduced set of domain

experts can be motivated to rate items

● Cold Start problem● Experts rate items as

soon as they are available

● Scalability● Dataset is several order of

magnitudes smaller

● Privacy● Recommendations can be

computed locally

Architecture of the approach

Some implementations

● A distributed Music Recommendation engine

J. Ahn and X. Amatriain et al. "Towards Fully Distributed and Privacy-preserving Recommendations via Expert Collaborative Filtering and RESTful Linked Data", Web Intelligence '10

Expert Music Recommendations

Powered by...

Some implementations (II)

● A geo-localized Mobile Movie Recommender iPhone App

J. Bachs and X. Amatriain et al. "Geolocated Movie Recommendations based on Expert Collaborative Filtering", Recsys '10

Geo-localized Expert Movie Recommendations

Powered by...

Context Overload

≠

Mobile phones are “personal”

Mobile users tend to seek “fresh” content

Where is the nearest florist?

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

Interesting things close to me?

Events near me?

Lost or in an unfamiliar place?

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

L. Baltrunas, X. Amatriain "Towards Time-Dependant Recommendation based on Implicit Feedback", in CARS (Context-aware Recommender Systems Workshop) Recsys '09

Multiverse Recommendation

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

A. Karatzoglou, X. Amatriain, L. Baltrunas, N. Oliver "Multiverse Recommendation: N-dimensional Tensor Factorization for Context-aware Collaborative Filtering", 2010 ACM Recsys Conference

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

– ....

N. Tintarev, A. Flores, X. Amatriain (2010)"Off the beaten track - a mobile field study exploring the long tail of mobile tourist recommendations", 2010 Mobile HCI

Master Planner

● I am in SB, it's March 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

Summary

➢ We need to build tools and approaches to help people navigate the abundance of media and information

➢ Recommender systems can help by leveraging the wisdom of the crowds

➢ But...➢ User feedback is not always our ground truth➢ Crowds are not always wise and we are better off

using experts➢ Context is becoming part of the content itself

Co-authors

● Josep M. Pujol and Nuria Oliver (Telefonica) worked on Natural Noise and Wisdom of the Few projects

● Neal Lathia (UCL, London), Haewook Ahn (KAIST, Korea), Jaewook Ahn (Pittsbourgh Univ.), and Josep Bachs (UPF, Barcelona) on Wisdom of the Few

● Linas Baltrunas (Bolzano U., Italy), Alexandros Karatzoglou, Paulo Villegas, Toni Cebrian (Telefonica) worked on contextual

● Miquel Ramirez (UPF, Barcelona) and Nava Tintarev (Telefonica) worked on Tourist Recommendations.

Conclusions

➢ Whether you are an engineer, an artist or a scientist (or all of the above), it is important to keep the “user” in mind➢ Who are my “users”? (end-user, public, other

scientists, a grant agency...)➢ How will the output of my work affect users?● How can I obtain feedback from them?➢ How can I use it?➢ ...➢

Thanks!

Questions?

Xavier Amatriainxar@tid.es

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

@xamat