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Cinemappy: a Context-aware Mobile App for Movie Recommendations boosted by DBpedia
 

Cinemappy: a Context-aware Mobile App for Movie Recommendations boosted by DBpedia

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    Cinemappy: a Context-aware Mobile App for Movie Recommendations boosted by DBpedia Cinemappy: a Context-aware Mobile App for Movie Recommendations boosted by DBpedia Presentation Transcript

    • 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 ostuni@deemail.poliba.it, t.dinoia@poliba.it, roberto.mirizzi@hp.com, romito@deemail.poliba.it, disciascio@poliba.it1Politecnico di Bari 2HP LabsVia Orabona, 4 1501 Page Mill Road70125 Bari (ITALY) Palo Alto, CA (US) 94304 SeRSy 2012 – International Workshop on Semantic Technologies meet Recommender Systems & Big Data ISWC 2012 November 11, 2012 Boston, USA
    • 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 SeRSy 2012 – International Workshop on Semantic Technologies meet Recommender Systems & Big Data ISWC 2012 November 11, 2012 Boston, USA
    • Recommender Systems 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.]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 SeRSy 2012 – International Workshop on Semantic Technologies meet Recommender Systems & Big Data ISWC 2012 November 11, 2012 Boston, USA
    • Two main approachesCollaborative filteringThe problem of collaborative filtering is to predict how well a user will like an itemthat he has not rated yet, given a set of historical preference judgments for acommunity of users. 5 ? current user ? 1 3 4 5 SeRSy 2012 – International Workshop on Semantic Technologies meet Recommender Systems & Big Data ISWC 2012 November 11, 2012 Boston, USA
    • Two main approachesContent-basedContent-based RSs recommend items to a user based on their description and onthe profile of the user’s interests Movie’s attributes 1 • Cast 4 • Genres • Director …. SeRSy 2012 – International Workshop on Semantic Technologies meet Recommender Systems & Big Data ISWC 2012 November 11, 2012 Boston, USA
    • Taking into account user’s context: Context Aware RSsWhat 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 andthe applicationExternal factors:Time, weather, season,….User factors:Mood, activity, company, location,.. SeRSy 2012 – International Workshop on Semantic Technologies meet Recommender Systems & Big Data ISWC 2012 November 11, 2012 Boston, USA
    • Using Linked Data in Recommender Systems Content BasedMitigation of Limited Content Analysis (huge amount of available knowledge)No Content Analyzer required (structured data available) Collaborative filteringMitigation of cold start via hybridizationCross domain recommendationsKnowlegeable explanations SeRSy 2012 – International Workshop on Semantic Technologies meet Recommender Systems & Big Data ISWC 2012 November 11, 2012 Boston, USA
    • Cinemappy: a mobile context-aware and content-based RS of movie-cinema bundlesSuggest to the user movies to be watched in theaters taking intoaccount user preferences and user context (position, companion,..) Like/dislikeExplicitCompanyContext Movie, Cinema bundle SeRSy 2012 – International Workshop on Semantic Technologies meet Recommender Systems & Big Data ISWC 2012 November 11, 2012 Boston, USA
    • Cinemappy Architecture SeRSy 2012 – International Workshop on Semantic Technologies meet Recommender Systems & Big Data ISWC 2012 November 11, 2012 Boston, USA
    • Contextual factorsExplicit•Companion : family, friends, partner, by myself is better, coworker, noneImplicit•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 SeRSy 2012 – International Workshop on Semantic Technologies meet Recommender Systems & Big Data ISWC 2012 November 11, 2012 Boston, USA
    • Recommender EngineContextual Pre-filteringContent-basedContextual Post-filtering RECOMMENDER Pre-filtering CB RS Post-filtering SeRSy 2012 – International Workshop on Semantic Technologies meet Recommender Systems & Big Data ISWC 2012 November 11, 2012 Boston, USA
    • 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.  profile (u , cmp )  m j ,v j  v j = 1 if u likes m j with companion cmp, v j = -1 otherwise    SeRSy 2012 – International Workshop on Semantic Technologies meet Recommender Systems & Big Data ISWC 2012 November 11, 2012 Boston, USA
    • Content-based Recommendations with DBpedia (i)We predict the rating using a Nearest Neighbor Classifier  v j  sim(m j , mi ) ?? m j  profile ( u ,cmp ) rPreF (ucmp , mi )  profile(u, cmp )But…Our movies are RDF resources…how to compute similarities between RDF resources? SeRSy 2012 – International Workshop on Semantic Technologies meet Recommender Systems & Big Data ISWC 2012 November 11, 2012 Boston, USA
    • Computing similarity in DBpedia SeRSy 2012 – International Workshop on Semantic Technologies meet Recommender Systems & Big Data ISWC 2012 November 11, 2012 Boston, USA
    • Vector Space Model for DBpedia (i) Righteous Kill Heat Righteous Kill Al Pacino Robert De Niro Brian Dennehy Heat Robert De Niro starring Al Pacino Brian Dennehy John AvnetSerial killer films Heist films Crime films genre subject/broader Drama director starring Crime films Heat Brian Dennehy Drama John Avnet Righteous Kill Heist films Robert De Niro Al Pacino Serial killer filmsT.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. SeRSy 2012 – International Workshop on Semantic Technologies meet Recommender Systems & Big Data ISWC 2012 November 11, 2012 Boston, USA
    • Vector Space Model for DBpedia (ii) Robert Brian Al Pacino STARRING De Niro Dennehy (a1) (a2) (a3) Righteous    Kill (m1) Heat (m2)   Righteous Kill Heat wactorx ,moviey  tf actorx ,moviey  idf actorx Righteous Kill (m1) wa1,m1 wa2,m1 wa3,m1 Heat (m2) wa1,m2 wa2,m2 0 SeRSy 2012 – International Workshop on Semantic Technologies meet Recommender Systems & Big Data ISWC 2012 November 11, 2012 Boston, USA
    • Vector Space Model for DBpedia(iii) wa1 ,m1  wa1 ,m2  wa2 ,m1  wa2 ,m2  wa3 ,m1  wa3 ,m2simstarring (m1 , m2 )  wa1 ,m1  wa2 ,m1  wa3 ,m1  wa1 ,m2  wa2 ,m2  wa3 ,m2 2 2 2 2 2 2  starring  simstarring (m1 , m2 ) +  director  simdirector (m1 , m2 ) +  subject  simsubject (m1 , m2 ) + … = sim(m1 , m2 ) SeRSy 2012 – International Workshop on Semantic Technologies meet Recommender Systems & Big Data ISWC 2012 November 11, 2012 Boston, USA
    • 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   p  sim p (m j , mi )  m j  profile ( u ,cmp ) vj  p P rPreF (ucmp , mi )  profile(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. SeRSy 2012 – International Workshop on Semantic Technologies meet Recommender Systems & Big Data ISWC 2012 November 11, 2012 Boston, USA
    • 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. ( h c cl  ar  ap )r ( ucmp , mi )  1  rPreF (ucmp , mi )   2  5h (hierarchy): it is equal to 1 if the cinema is in the same city of the currentuser 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, 0otherwise.ap (anchor-point proximity): it is equal to 1 if the cinema is close to the usershouse or the users office, 0 otherwise. SeRSy 2012 – International Workshop on Semantic Technologies meet Recommender Systems & Big Data ISWC 2012 November 11, 2012 Boston, USA
    • Recommendations What? Where? SeRSy 2012 – International Workshop on Semantic Technologies meet Recommender Systems & Big Data ISWC 2012 November 11, 2012 Boston, USA
    • Conclusion & Future directionsWe 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) SeRSy 2012 – International Workshop on Semantic Technologies meet Recommender Systems & Big Data ISWC 2012 November 11, 2012 Boston, USA
    • Q&AStay tuned! Soon Cinemappy available on the Android MarketWe acknowledge partial support of HP IRP 2012. Grant CW267313. SeRSy 2012 – International Workshop on Semantic Technologies meet Recommender Systems & Big Data ISWC 2012 November 11, 2012 Boston, USA