politecnico di milano top-n recommendations on unpopular items with contextual knowledge paolo...

Politecnico di Milano

Top-N recommendations on Unpopular Items

with Contextual Knowledge

Paolo CremonesiAntonio TripodiRoberto Turrin

Politecnico di MilanoContentWise

Today recommendations,based on your personal taste, are:

From this….

To this

iTV with personalization

Personalization: how it works

USERDATA

USER’S TASTE

FRUTIONS ANDRATINGS

CONTENTMETADATA

RECOMMENDER

SYSTEM

CONTENTRECOMMENDATIONS

4

CustomersService Provider Network ProviderContent Provider

IPTV architecture

Head end

VOD

Set-top-box(decoder)

user u

item i

5

Single domain


• Two ideas– One aII from Ricci– Closure (on UU and II)


• Inserire disegnino IPTV• Recommender systems can be divided into two

families– Content-Based Filtering– Collaborative Filtering

• CB algorithms are preferred– Do not rely on metadata (very difficult to obtain in the

TV domain)– Quality has been proved to be better in terms of

accuracy an serendipity, if the system has been trained with enough data


• CF algorithms can be classified into – Model-based (able to deal with new users)– Non nodel-based (not able to deal with new users)




Paolo CremonesiPaolo Garza

Elisa QuintarelliRoberto Turrin


Short version


Thanks for your attention

Q&A

For any further information, please contact

Paolo [email protected]




Paolo CremonesiPaolo Garza

Elisa QuintarelliRoberto Turrin


Long version


Research objectives

• Focus– Top-N recommendation task

• Goal– Improving accuracy– Providing explanation

• Requirements– Modularity (algorithm-independent)– Fast on-line recommendations


Algorithms

23

Accu

racy CorNgbr

Non-personalized

Neighborhood Latent factors

NNCosNgbr

AsySVD PureSVD

Collaborative

TopPop MovieAvg


TopPop and MovieAvg

24

Accu

racy CorNgbr

Non-personalized


NNCosNgbr

AsySVD PureSVD

Collaborative

TopPop MovieAvg

Recommends the top-N popular items (i.e., the most rated items), regardless the user preferences and taste


TopPop

• Pirates of the Caribbean: The Curse of the Black Pearl

• Forrest Gump• The Lord of the Rings: The Two Towers• The Lord of the Rings: The Fellowship of the

Ring• The Sixth Sense


Collaborative - Neighborhood

26

Accu

racy CorNgbr

Non-personalized


NNCosNgbr

AsySVD PureSVD

Collaborative

TopPop MovieAvg

They recommend items according to the approach: “who bought this also bought this..”Amazon like …


Collaborative – Latent factors

27

Accu

racy CorNgbr

Non-personalized


NNCosNgbr

AsySVD PureSVD

Collaborative

TopPop MovieAvg

They recommend items on the basis of an advanced representation of users and items in a low-dimensional feature space


Contextual recommendations

• Pre-filtering– L.Baltrunas, F.Ricci

RecSys'09• Post-filtering– U.Panniello, A.Tuzhilin, M.Gorgoglione, ...

RecSys'09• Contextual modeling– M.Domingue, A.Jorge, C.Soares

RecSys'09– C. Palmisano, A.Tuzhilin, M.Gorgoglione

IEEE Trans. Knowl. Data Eng., 2008


Association rules

• Data mining technique

• Uses “frequency based” approach to find conditional probability of events

• Forrest Gump and Nikita → Avatar


Association rules

• X→Y• X = previously watched movie(s)• Y = movie(s) the user will likely appreciate• Quality of association rules: – Support: frequency of the rule– Confidence: conditional probability of Y given X

• Benefits (by definition)– best recommendations in terms of accuracy


Association rules and RS

• Sarwar et al. Analysis of recommendation algorithms for e-commerce, EC 2000

• Computational requirements– theoretically we should test for all the possible

combinations of items in X and Y• Portfolio effect– most rules find the same small set of consequents– recommendations are biased toward obvious

items


Portfolio effect


Portfolio effect

• Which is the most simple and yet most effective association-rule based recommender system?


Portfolio effect

• Which is the most simple and yet most effective association-rule based recommender system?

• TopPop


Recall on Netflix

Algorithm Recall at 10

PureSVD50 0.48NNCosNgbr 0.45AsySVD 0.30TopPop 0.28CorNgbr 0.15MovieAvg 0.12


Recall on Netflix

Algorithm Recall at 10 Recall at 10long tail only

PureSVD50 0.48 0.30NNCosNgbr 0.45 0.28AsySVD 0.30 0.30TopPop 0.28 0.02CorNgbr 0.15 0.35MovieAvg 0.12 0.12

Removed the most popular itemsaccounting for 33% of ratings


Measured perceived quality

• Users’ judgments on Accuracy and Novelty

• Participants:30 users per 7 experimental condition → 210 users overall

• Profile: 20 - 50 years old male: 54% - female: 46%

Perceived relevance

Perceived novelty


Context recommender system

• Traditional Recommender

System

• Users’ contexts - Items’ characteristics

• Contextual Rule Mining

• Recommendations • Contextual rules

• Contextual Post-filtering

• Contextual Recommendations

• Users - Items


Experiments: rules mining

• Movielens: 1 M ratings– 1000 users– 1700 items

• Context– # age ranges = 7– # gender = M/F

• Movie features– # genres = 18

• Rules mined with FP-growth• Min support = 1000


Goal

• Identify correlations between user’s context and item characteristics

• Filter predictions performed by a traditional recommender


Inputs to the system

• Input to the recommender system

URM

• Input to the contextual rule miner

CFM – User context × Item features– number of ratings users in context c gave to items

with feature f


Creation of the transactional dataset

• UCM → Transactional dataset•

Example:A rating given by a Male with age [20-25] to a fantasy movie

(gender = M)(age = [20-25])(genre = fantasy)

is included in the transactional dataset


Rule mining: example

• The following two rules are extracted for the context (gender = M):

(gender = M) ) (genre = horror)(gender = M) ) (genre = action)

• It follows that only horror and action movies can be recommended to male users


Example of rules …

Gender Age Genre Prob. SupportF 35-44 Drama 35% 17000

Comedy 32% 16000Romance 18% 9000Children’s 8% 4000Musical 5% 2500Animation 4% 2000Mystery 4% 2000Fantasy 3% 1500



Gender Age Genre Prob. SupportM 35-44 Action 23% 34000

Thriller 16% 24000Sci-Fi 14% 21000Adventure 12% 17000War 7% 10000Horror 6% 9000Mystery 3% 5000Western 2% 3000Noir 2% 3000


Two options

• Keep all of the rules

• Keep only rules with a “large” confidence15%

• In any case, we keep only rules with a large support (>1000 ratings)



Gender Age Genre Prob. SupportF 35-44 Drama 35% 17000

Comedy 32% 16000Romance 18% 9000Children’s 8% 4000Musical 5% 2500Animation 4% 2000Mystery 4% 2000Fantasy 3% 1500



Gender Age Genre Prob. SupportM 35-44 Action 23% 34000

Thriller 16% 24000Sci-Fi 14% 21000Adventure 12% 17000War 7% 10000Horror 6% 9000Mystery 3% 5000Western 2% 3000Noir 2% 3000


Results


Recall at 5 on long-tail items

Long tail Without context With contextPureSVD 21% 30%AsySVD 7% 12%NNCosNgbr 10% 24%CorNgbr 4% 9%TopPop 0.1% 8%MovieAvg 1% 5%

Removed 5% of the most popular itemsAccounting for 33% of ratings


Recall with non-personal methods


Recall with neighborhood methods


Recall with latent-factors methods


Thanks for your attention

Q&A

For any further information, please contact

Paolo [email protected]

politecnico di milano top-n recommendations on unpopular items with contextual knowledge paolo...

Documents

toppop slide

milano contentwise slide

data slide

taste slide

personalization slide

sense slide

milano portfolio effect

terms of accuracy slide