iptv recommender systems

IPTV Recommender Systems

Paolo Cremonesi

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Paolo Cremonesi - Recommender Systems

Agenda

• IPTV architecture• Recommender algorithms• Evaluation of different algorithms• Multi-model systems

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Valentino Rossi

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CustomersService Provider Network ProviderContent Provider

IPTV architecture

Live TV

VOD

Set-top-box(decoder)

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IPTV architecture

• IPTV is a video service supplied by a telecom service provider that owns the network infrastructure and controls content distribution over the broadband network for reliable delivery to the consumer (generally to the TV/IP STB).

• ServicesBroadcast TV (BTV) services which consist in the simultaneous reception by the users of a traditional TV channel, Free-to-air or Pay TV. BTV services are usually implemented using IP multicast protocols. Video On Demand (VOD) services, which consist in viewing multimedia contents made available by the Service Provider, uponrequest. VOD services are usually implemented using IP unicastprotocols.

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CUSTOMER FRUSTRATION

CUSTOMER PURCHASES

CUSTOMERS FACEDIFFICULTIES FINDING THE “RIGHT” CONTENTHUNDREDS

LIVECHANNELS

THOUSANDSVOD

ITEMS

IPTV Platform: Now

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Today recommendations,based on your personal taste, are:

From this….

To this.

IPTV Platform: with a recommender systems

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IPTV recommender needs

• Improve user satisfaction

• Sell new content to usersVOD Pay-per-view channels

• Targeting advertisement

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Agenda


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Recommender System: how it works

USERDATA

USER’S TASTE

FRUTIONS ANDRATINGS

CONTENTMETADATA

RECOMMENDER

SYSTEM

CONTENTRECOMMENDATIONS

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Problem formulation

RecommenderUsers ratings Items metadata

•Item1•Item2•Item3

•.•.•.

•ItemX

Ranked list

Top N

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Similar ItemsUsers with

similar taste

Recommendation techniques

CollaborativeFiltering

Content-basedFiltering

Userbased

Itembased

Recommenderalgorithms

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Memory vs. model based

XContent-based

XDimensional-reduction

XItem-based

XUser-based

Modelbased

Memorybased

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Collaborative Filtering

4 5

2 2

?3

Neighborhood

User-basedsimilar users rate an item similarly

Item-basedsimilar items are rated by a user similarly

NB: similarity means correlation

User-basedsimilar users rate an item similarly

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Collaborative filtering: User Rating Matrix

User

Item

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I1 I2 I3 I4

U1 3 4 0 1

U2 2 2 1 0

U3 2 0 0 4

U4 1 5 0 1

U5 3 0 1 0

Explicit URM

User rating matrix URM

I1 I2 I3 I4

U1 0 1 0 1

U2 0 0 1 1

U3 1 1 0 0

U4 1 1 0 1

U5 0 1 1 1

Implicit URM

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Dimensional-reduction collaborative model

• items and users can be described by a number (K) of unknown features

• auf : describes if feature f is important for user u• bif : describes if feature f is present in item i• rui : rating assigned by (or estimated) user u to item i

rui =kf=1 auf · bif

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Singular Value Decomposition

A = U S VTVTR

A =

VkT

Rk Uk

Sk

m x n

m x n m x k k x k k x n

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UT ·U = I

V ·VT = I

R = U · S ·VT

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Rk : best rank-k approximation of R according to the Frobenious normnot according least square error!!

UTk ·Uk = I

Vk ·VTk = I

Rk = Uk · Sk ·VTk

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Folding-in

• New rows/columns of A are projected (folded-in) in the existing latent space without computing a new SVD

• e.g., a new user u

u’ = u Vk Sk-1

Ak

u

Uk

u’

Sk Vk

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Collaborative Filtering: pro & cons

• Pro: There is no need for content

• Cons:Cold Start: we needs to have enough users in the system to find a match.Sparsity: when the user/ratings matrix is sparse it is hard to find a neighbourhood.First Rater: cannot recommend an item that has not been previously rated anyone elsePopularity Bias: cannot recommend items to someone with unique tastes. Tends to recommend popular items (dataset coverage)

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Content-based Filtering

...mettendo a punto una scoperta che potrebbe portare al primo uso terapeutico della controversa procedura. Se gli studi animali si riveleranno promettenti, i ricercatori potrebbero cominciare a mettere alla prova le nuove cellule su occhi umani da qui a due anni...

Term 1

Term2

Term

3

• Similar items contain the same terms• The more a term occurs in an item, the more representative it is• The more a term occurs in the collection, the less representative it is

(i.e. it is less important in order to distinguish a specific item)

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Content-based filtering: Item-Content Matrix

Word

Item

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Content-based Filtering: techniques

User-item similarity

Term 1

Term2

Term

3

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Content-based Filtering: pro & cons

• Pro: No need for data on other usersNo cold-start or sparsity problems, neither first-rater

• Able to recommend to users with unique tastes• Able to recommend new and unpopular items

Can provide explanations about recommended itemsWell-known technology

• Cons:Requires a structured contentLower accuracyUsers tastes must be represented as a function of the contentUnable to exploit quality judgments of other users

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Content-based Filtering: Latent Semantic Analysis

A Ak

VkT

Uk

Sk=

Uk * sqrt (Sk)Vk * sqrt (Sk)

pseudo terms pseudo items

cosine

Ak

svd

-Terms in rows-Items in columns

m x n

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Recommender architecture

Items Storage Features extraction

Featuresrepresentation

Users Infer and learn profile

Interests/tastes representation

Compute user-item correlation

Items retrieval

Items recommendation

Resources management

Users management

Filter

feedback

Explicit vs implicit ratings

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Datasets

• User-item rating matrix23942 users564 movies56686 ratings

• Movie Meta-data (textual information)TitleGenreDirectorCastDuration…

Real datasets composed by movies and user fruitions, plus some extra information

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• Implicit vs Explicit• Come determinare il rating implicito

VODTV (EPG)

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Some problems with IPTV recommender

• Cold start

• Multi-language content(e.g., Switzerland)

• New user problem(user-based algorithms)

• New item problem(all collaborative algorithms)

• Semantic problem(e.g., house and home)

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Agenda


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Problem

• Many works do not describe clearly the methods used for performance evaluation and model comparison

• Different dataset partition methodology and evaluation metrics lead to divergent results

• The Netflix prize has improperly focused the research attention onHold-outRMSE

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Objective

• Design a new methodology to compare different algorithms according to

how often the user watches the TV(length of user profile)

if the user prefers “blockbuster” movies(user preference versus popular or unpopular movies and programs)

• Design a multi-model system

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Metrics

• Error metricsMean Square Error(MSE)Root Mean Square Error (RMSE)Mean Absolute Error (MAE)

Only for explicit datasetsTop-N recommender systems

• Accuracy metricsRecallPrecisionFalloutF-measure

☺ Both implicit and explicit datasets

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Accuracy metrics

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Accuracy metrics

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Accuracy metrics

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Netflix dataset: test user profile

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Netflix dataset: Global effects algorithm

RMSE: 0.95Recall: 1%F-measure: 0.01


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Netflix dataset: Adjusted cosine algorithm



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Netflix dataset: SVD algorithm



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Quality evaluation

• Focus on future performance on new data• Proper partitioning of original data set into:

training settest set

• Test set must be different and independent from training set• Active user: should be left out of the model

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Hold-out

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Leave-one-out

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K-fold

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Agenda


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Recommender system architecture

Web Services Items’ Content

(ICM)

Users’ Ratings (URM)

Batch Processing

Real-time Recommendation

Model Repository

Inputs Real time calls

STB

server

STB client

STB client

…

Business

Rules


Proposed approach

• Batch system

Statistical analysis of the datasetDefinition of a number of modelsAccuracy evaluation for different user profiles

• Run-time system

User profile analysisSelection of best candidate modelRecommendation


Multi-model recommender engine

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Dataset statistical analysis (example)

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100 101 102 103 1040

0.25

0.5

0.75

1

Position of the items in the top-rated

Per

cent

age

of ra

ted

item

s in

the

top-

rate

d

NMMLNF

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20 or more

10...19 2...9

Popular

Non-Popular

User groups Item popularity

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Popular vs. unpopular: SVD algorithm - NF

0 200 400 600 800 10000

0.05

0.1

0.15

0.2

0.25

0.3

0.35

Latent size

Rec

all

allpopularunpopular

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Popular vs. unpopular: SVD algorithm - NM

50 100 200 300155

0.05

0.1

0.15

0.2

0.25

Latent size

Rec

all

allpopularunpopular

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User profile length – NM recall

All

Group SVD Cos -like NBN_S NBN_I NBN_U2-9 11,21% 19,35% 19,65% 17,23% 21,65%

10 -19 11,23% 13,11% 12,09% 13,62% 12,60%20 -inf 9,91% 8,01% 6,45% 6,65% 6,52%

Group NBN_UGroup NBN_U

Popular


10 -19 24,12% 27,12% 24,61% 27,36% 25,59%20 -inf 25,72% 22,71% 20,71% 21,14% 20,93%

Group NBN_UGroup NBN_U

Unpopular


10 -19 1,23% 0,19% 0,56% 0,25%20 -inf 10,14% 0,70% 0,01% 0,10% 0,01%

Group SVD NBN_UGroup SVD NBN_U

10,01%

15,94%Best average algorithm (item-based)

20,92%Multi-model (overall)

iptv recommender systems

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