cinemappy: a context-aware mobile app for movie recommendations boosted by dbpedia

SeRSy 2012 – International Workshop on Semantic Technologies meet Recommender Systems & Big Data

ISWC 2012 November 11, 2012 Boston, USA

CINEMAPPY: A CONTEXT-AWARE MOBILE APP FOR MOVIE RECOMMENDATIONS BOOSTED BY DBPEDIA

Vito Claudio Ostuni1, Tommaso Di Noia1, Roberto Mirizzi2,

Davide Romito1, Eugenio Di Sciascio1

[email protected], [email protected], [email protected], [email protected], [email protected]

2HP Labs 1501 Page Mill Road Palo Alto, CA (US) 94304

1Politecnico di Bari Via Orabona, 4 70125 Bari (ITALY)



Outline

What are Recommender Systems?

Collaborative filtering & Content-based algorithms

Taking into account user’s context: Context Aware RSs

Why should we use LOD to feed them?

Cinemappy: a mobile context-aware and content-based Recommender System for movie-cinema bundles

Architecture

Contextual factors

Recommender Engine

Conclusion



Recommender Systems

Input Data: A set of users U={u1, …, uN} A set of items I={i1, …, iM} The rating matrix R=[ru,i]

Problem Definition:

Given user u and target item i Predict the rating ru,i

A definition Recommender Systems (RSs) are software tools and techniques providing suggestions for items to be of use to a user. [F. Ricci, L. Rokach, B. Shapira, and P. B. Kantor, editors. Recommender Systems Handbook. Springer, 2011.]



Two main approaches

Collaborative filtering The problem of collaborative filtering is to predict how well a user will like an item that he has not rated yet, given a set of historical preference judgments for a community of users.

current user

??

5

3 1

4

5



Two main approaches

Content-based Content-based RSs recommend items to a user based on their description and on the profile of the user’s interests

Movie’s attributes • Cast • Genres • Director

….

4

1



Taking into account user’s context: Context Aware RSs

What do we mean by context? - any information that can be used to characterize the situation of an entity

- any information or conditions that can influence the user perception

- any information that can characterize the interaction between a user and the application

External factors: Time, weather, season,….

User factors: Mood, activity, company, location,..



Using Linked Data in Recommender Systems

Content Based Mitigation of Limited Content Analysis (huge amount of available knowledge) No Content Analyzer required (structured data available)

Collaborative filtering Mitigation of cold start via hybridization

Knowlegeable explanations

Cross domain recommendations



Cinemappy: a mobile context-aware and content-based RS of movie-cinema bundles

Explicit Company Context

Movie, Cinema bundle

Suggest to the user movies to be watched in theaters taking into account user preferences and user context (position, companion,..)

Like/dislike



Cinemappy Architecture



Contextual factors

Explicit •Companion : family, friends, partner, by myself is better, coworker, none

Implicit •Time : all the movies scheduled before the current time, plus the time to get to the theatre, have to be discarded

•Geographical relevance : depending on the current location of the user, the system should be able to suggest movies to watch in cinemas close/relevant to them and discard the farther ones even if they may result more appealing with respect to the user preferences.

Some criteria of geographical relevance: •Cluster: a multiplex could be more interesting than a normal cinema

•Co-location: a cinema close to a pub could be more useful

•Anchor point proximity: a cinema close home could be more easy to get to



Contextual Pre-filtering

Content-based

Contextual Post-filtering

Recommender Engine

Pre-filtering

Post-filtering

CB RS

RECOMMENDER



Contextual Pre-filtering

Time and distance contextual factors For each user u, the set of movies Mu (set of movies that the system can recommend to u) is defined as containing the movies scheduled in the next d days in theaters in a range of k kilometers around the user position. The system can recommend only movies in Mu.

Companion contextual factor Regarding this context we use the micro-profiling approach. To the user u is associated a set of different profiles each one related to a specific value cmp of the companion context. ( , ) , = 1 if u likes with companion cmp, = -1 otherwise profile u cmp m v v m v

j j j j j



We predict the rating using a Nearest Neighbor Classifier

( , )

PreF

( , )

( , )( , )

j

j j i

m profile u cmp

cmp i

v sim m m

r u mprofile u cmp

But…Our movies are RDF resources… how to compute similarities between RDF resources?

Content-based Recommendations with DBpedia (i)

??



Computing similarity in DBpedia



Vector Space Model for DBpedia (i)

Righteous Kill

Robert De Niro

John Avnet Serial killer films

Drama starring

director subject/broader

genre

Heat

Al Pacino Brian Dennehy

Heist films

Crime films

Rig

hte

ou

s K

ill

Ro

be

rt D

e N

iro

Joh

n A

vnet

Seri

al k

ille

r fi

lms

Dra

ma

He

at

Al P

acin

o

Bri

an D

en

ne

hy

He

ist

film

s C

rim

e f

ilms

starring

Ro

be

rt D

e N

iro

A

l Pac

ino

B

rian

De

nn

eh

y

Righteous Kill Heat

T.Di Noia, R. Mirizzi, V. C. Ostuni, D. Romito, and M. Zanker. Linked open data to support content-based recommender systems. In 8th International Conference on Semantic Systems (I-SEMANTICS 2012), ICP. ACM Press, 2012.



Vector Space Model for DBpedia (ii)

Righteous Kill

STARRING Al Pacino

(a1)

Robert De Niro

(a2)

Brian Dennehy

(a3)

Righteous Kill (m1)

Heat (m2)

Heat

xyxyx actormovieactormovieactor idftfw ,,

Righteous Kill (m1) wa1,m1 wa2,m1 wa3,m1

Heat (m2) wa1,m2 wa2,m2 0



Vector Space Model for DBpedia(iii)

1 2( , )starring starringsim m m

1 2( , )director directorsim m m

1 2( , )subject subjectsim m m

+

+

1 2( , )sim m m

+ … =

1 1 1 2 2 1 2 2 3 1 3 2

1 1 2 1 3 1 1 2 2 2 3 2

, , , , , ,

1 22 2 2 2 2 2

, , , , , ,

( , )a m a m a m a m a m a m

starring

a m a m a m a m a m a m

w w w w w wsim m m

w w w w w w



Content-based Recommendations with DBpedia (ii)

We predict the rating using a Nearest Neighbor Classifier wherein the similarity measure is a linear combination of property-dependent similarities

( , )

PreF

( , )

( , )( , )

j

p

p j i

p

j

m profile u cmp

cmp i

sim m m

vP

r u mprofile u cmp

What about computing alpha coefficients? T.Di Noia, R. Mirizzi, V. C. Ostuni, D. Romito, and M. Zanker. Linked open data to support content-based recommender systems. In 8th International Conference on Semantic Systems (I-SEMANTICS 2012), ICP. ACM Press, 2012.

What about other content-based algorithms with LOD? T. Di Noia, R. Mirizzi, V. C. Ostuni, and D. Romito. Exploiting the web of data in model-based recommender systems. In 6th ACM Conference on Recommender Systems (RecSys 2012). ACM, ACM Press, 2012.



Contextual Post-filtering

Regarding the geographical criteria we apply post-filtering in order to re-rank the recommendations considering these criteria. For each criterion we introduce a binary variable which means the absence or presence of that criteria. ( )( , ) ( , )1 PreF 2 5

h c cl ar apr u m r u mcmp i cmp i

h (hierarchy): it is equal to 1 if the cinema is in the same city of the current user position, 0 otherwise; c (cluster ): it is equal to 1 if the cinema is part of a multiplex cinema, 0 otherwise; cl (co-location): it is equal to 1 if the cinema is close to other POIs, 0 otherwise; ar (association-rule): it is equal to 1 if the user knows the price of the ticket, 0 otherwise. ap (anchor-point proximity): it is equal to 1 if the cinema is close to the user's house or the user's office, 0 otherwise.



Recommendations

What?

Where?



Conclusion & Future directions

We have presented Cinemappy: a context-aware content-based recommender system for movies and movie theaters suggestions.

The main features are:

Android App

The Content-based Recommender is boosted by DBpedia localized graphs (IT and EN)

Several contextual factors have a fundamental role in the recommendations. Some of them are geographic criteria that go beyond the simple geographic distance.

We are currently working on:

Exploiting several implicit form of user feedbacks

Improving the recommendation with a hybrid approach (content-based and collaborative filtering)



Q & A

We acknowledge partial support of HP IRP 2012. Grant CW267313.

Stay tuned! Soon Cinemappy

available on the Android Market

cinemappy: a context-aware mobile app for movie recommendations boosted by dbpedia

Technology

recommender systems

international workshop

user movies

user u

user factors

current user

user context position

user perception