AN EVENT DISTRIBUTION PLATFORM FORRECOMMENDING CULTURAL ACTIVITIES

Toon De Pessemier, Sam Coppens, Erik Mannens, Simon Dooms, Luc MartensIBBT, Ghent University, Gaston Crommenlaan 8, Ghent, Belgium

{toon.depessemier, sam.coppens, erik.mannens, simon.dooms, Luc1.martens}@ugent.be

Kristof GeebelenIBBT, K.U. Leuven, Celestijnenlaan 200A, Leuven, Belgium

kristof.geebelen@cs.kuleuven.be

Keywords: Event recommendation, information filtering, content-based recommendation, service-oriented architecture

Abstract: Today, people have limited leisure time which they want to fill in according to their interests. At the same time,cultural organisations offer an enormous amount of activities via their websites. This scarcity of time and theabundance of cultural events reinforce the necessity of recommender systems that assist end-users in discov-ering events which they are likely to enjoy. However, traditional recommender systems can not cope withevent-specific restrictions such as the availability, time and location of cultural activities. Moreover, aggre-gating the events, collecting consistent metadata, and enriching these metadata with cross-domain knowledgepose additional challenges for the conventional distribution and recommender systems. In this paper, we showhow personalised recommendation, content-based filtering, and distribution of events can be enabled by theenrichment of events metadata via open linked data sets available on the web of data. For consistency acrossseveral events providers, we propose an event model using an RDF/OWL representation of the EventsML-G2standard. Integrating these various functionalities as an extendable bus architecture provides an open, user-friendly event distribution platform that offers the end-user a tool to access useful event information that goesbeyond basic information retrieval.

1 INTRODUCTION

Nowadays, people tend to consult interactive and dy-namic information from the Internet, rather than statichard-copy press. This shift applies to the culturalscene as well: cultural organisations provide detailedwebsites which may help to decide how to spendleisure time optimally. Furthermore, the role of thesecultural websites is evolving from merely informationprovider to a guide that puts structure and emphasison the demand and supply of cultural events and theiraccompanying assets. To deal with the vast and com-plex structure of cultural events and leisure activities,we believe that an event model should be used as thefundamental basis in organizing and accessing cul-tural event information. To handle the overload of in-formation, a recommender system based on this eventmodel is essential to assist users in finding relevantactivities.

Our ideas are demonstrated in the context of the

end-to-end event distribution platform CUPID1 whichis implemented for the Flemish cultural scene. Usingmultiple information sources, the events are aggre-gated and enriched with additional knowledge fromlarge linked data sets. The metadata of these events,described by the EventsML-G2 standard, togetherwith the enriching multimedia assets enable content-based filtering, recommendation and distribution ofthe events.

The remainder of this paper is organized as fol-lows: Section 2 elaborates on aggregating the eventsfrom various decentralized parties and the enrich-ment based on available knowledge sources. Sec-tion 3 describes the utilized EventsML-G2 (Inter-national Press Telecommunications Council, 2009)standard completed with its conceptualisation in anOWL (McGuinness and van Harmelen, 2004) ontol-ogy as a unifying (meta)data model. This event de-scription is used in the recommender module, whichis discussed in Section 4, to generate personal sug-

1https://projects.ibbt.be/cupid

gestions. In Section 5 we present the workflow-basedbus-infrastructure that is employed to integrate thedifferent functionalities to a continuous flow of cul-tural events. Finally, we offer a brief conclusion onour research work in Section 6.

2 AGGREGATION &ENRICHMENT

In a first phase, event aggregation is performed; i.e.the retrieval of primary information of cultural activi-ties from different event organisers. This primary in-formation is defined as the minimal information thatis necessary to identify and locate the event. In a nextphase, during content enrichment, primary informa-tion on cultural activities can be extended with moresubstantive and facilitating information. Examples offacilitating information are ‘how the weather will be’or ‘how to get to the event with public transport’. Sub-stantive information may include background infor-mation on the artist, press articles, images, etc.

A lot of event information is distributed by variouscontent providers all over the Internet. (The Flem-ish content providers who cooperated in this projectare CultuurNet Vlaanderen and Vooruit). They pub-lish their events on blogs, web sites, calendars, so-cial networks, etc. using different formats like sim-ple HTML2, iCalendar3 or RSS4. As a result, themain challenge of content aggregation in the contextof events is to provide a framework that supports theaggregation of data from heterogeneous data sourceswhich use different standards to publish their events.

To deal with these different incoming data for-mats, a transformation is required to convert the eventdata from their original format to the internal eventontology, discussed in Section 3. For this purposewe use an XSL transformations engine. XSLT5 is adeclarative, XML-based language used for the trans-formation of XML documents into other XML docu-ments. To support the aggregation of a new data for-mat in the future, the XSL style sheet has to be ex-tended with the mapping of this new format to the in-ternal event ontology. A double detection mechanismeliminates redundant data: events with same date andthe same location as existing events are considered asduplicate and will not be inserted into the database.

Enriching the events is realized by first extract-ing all the concepts out of the event description us-

2http://tools.ietf.org/html/rfc2854

3http://tools.ietf.org/html/rfc5545

4http://www.rss-specifications.com

5http://www.w3.org/TR/xsltis

ing linguistic processing. For this, we utilize Open-Calais and i.Know’s Information Forensics6 service.Afterwards, the extracted named entities are used forquerying other data sets and incorporating the re-trieved information. The extracted locations are em-ployed for querying Toerisme Vlaanderen, GeoNamesand DBpedia 7. Toerisme Vlaanderen is a Flemishtourism information point with a lot of informationon Flemish cities and regions, restaurants, hotels, andevents. The extracted organisations and persons areutilized for querying the DBpedia data set and Bib-Net’s data set. BibNet8 is a network of Flemish publiclibraries, which disseminates bibliographic informa-tion. The retrieved links are stored and offered asadditional information sources to the end-user. Byenriching the event information, the end-users get alot of extra data to make an informed choice. To-gether with the recommendations, this will help themto evaluate and select the events which match withtheir personal preferences. Another advantage of thisenrichment process is the semantic linking of eventinformation to other data sets which incorporates theevent information into the ‘Web of Data’.

3 EVENT MODEL

To acquire a standardised procedure for the dissem-ination of events and to enable content-based rec-ommendation techniques, a semantic metadata modelfor storing and exchanging event information is re-quired. To represent the events as structured data, var-ious standards exist. The most commonly used stan-dard for describing events is iCalendar. This standard,which describes events for personal management pur-poses, defines an event as anything with a scheduledamount of time on a calendar. Although, the iCalen-dar format is able to describe a socio-cultural event, itcan not express relations between the events. For in-stance, a festival can consist of many smaller events,i.e. concerts or music performances, which have to berelated.

In the Linked Open Data community, the mostused event model is the Music Ontology EventsModel (Centre for Digital Music - University ofLondon, 2007). AudioScrobbler (LinkingOpenData(W3C SWEO Community Project) - Centre for Dig-ital Music, 2007) of Last.FM utilizes this model todescribe events which are then linked to a user pro-

6http://www.opencalais.com, http://www.iknow.be

7http://www.toerismevlaanderen.be, http://www.geonames.org, http://

dbpedia.org8http://www.bibnet.be

file, for which FOAF9 is used. This way, an event ismodelled whenever a song is played. Originally, thismodel was intended to describe musical events, butdue to its simplicity and usability, it has been provenuseful in a wide range of contexts. This model de-scribes an event as anything that has a spatial andtemporal dimension. Such an event is described byits participating agents, its passive factors influenc-ing the event, its products as a consequence of theevent and a location in time as well as space. In addi-tion, this model allows describing relations betweenevents. However, its simplicity is also a disadvantagesince the model lacks some advanced features, likepricing information, more detailed relations betweenevents, minimum age for participation, etc., which areessential for describing events in our context.

Finally, the format that we adopted for mod-elling events is the EventsML-G2 (International PressTelecommunications Council, 2009) standard. Thisis a standard of the International Press Telecommuni-cations Council10 (IPTC) for conveying event infor-mation in a news industry environment. EventsML-G2 is intended for receiving, storing and exchang-ing event information from organisers as well as pub-lishing event information by news providers. Thismodel delivers the right context for our event descrip-tions since it allows describing events in different lan-guages, together with their relations, the pricing in-formation, the minimum age, etc.

Currently, the EventsML-G2 standard is describedas an XML (Bray et al., 2006) schema. In order to ap-ply semantic web techniques, we developed an OWL(McGuinness and van Harmelen, 2004) ontology ofthis standard. An alternative to make the EventsML-G2 schema usable in the Semantic Web Stack, is de-scribing the schema in RDFS (Brickley, 2004). How-ever, since OWL is a richer language for develop-ing models and permits to mix RDFS and OWL con-structs, we opted for describing the EventsML-G2 se-mantic ontology by using OWL. This OWL ontologyis published online on the website of the MultimediaLab research group of Ghent University11.

By describing all the aggregated events using thisontology, a common format is created for exposingthe event information. This common format acts asa unifying layer, relating all the information com-ing from different data providers. By providing asemantic ontology for these events, content-basedrecommendation algorithms are able to analyse theevents in detail, and additionally, semantic web tech-niques can be used. Moreover, this semantic ontol-

9http://xmlns.com/foaf/spec/

10http://www.iptc.org

11http://multimedialab.elis.ugent.be/ontologies/EventsML-G2/v1.0/

ogy allows complex queries for events by a SPARQL(Prud’hommeaux and Seaborne, 2007) endpoint andthe enrichment of event descriptions with informationfrom other data providers as described in Section 2.

EventsML-G2 has two manners for conveyingevent information: as a conceptItem or as a knowl-edgeItem. A conceptItem is aimed at describingan event solely. A knowledgeItem is intended forbundling a set of events which are managed as awhole. Given our context, i.e. publishing and recom-mending events from event organisers, we utilize theconceptItem to model the events. For interoperabilityissues, we modelled agents involved in an event, e.g.organisers or participants, using the FOAF ontology.This allows us to incorporate more easily informationabout the agents from other data sets afterwards. Thisadditional advantage is the main difference betweenour developed ontology and the EventsML-G2 speci-fication.

The descriptive metadata about the event is cap-tured by the Concept class, which stores a descriptionof the event, the title of the event, the event details, re-lations to other events, and the language of the eventdescription. The relations to other events can be de-scribed by owl:sameAs for stipulating that this eventis the same as another event, and by skos:related,skos:broader or skos:narrower for pinpointing the re-lations to other events. The EventDetails class con-tains a more complete event description and is linkedto ten other classes for storing detailed informationabout the event, namely: Dates, Location, Contact-Info, Agent, Language, Subject, OccurStatus, Regis-tration, ParticipationRequrement, and Media.

4 EVENT RECOMMENDATION

To handle the overload of information, a recom-mender system is necessary to assist users in find-ing the most relevant events. Traditionally, recom-mender systems have been categorized into two mainclasses: collaborative filtering (CF) and content-based(CB) methods. CF techniques are based on the hy-pothesis that a good method to find interesting con-tent is to search for other people who have similarinterests, and then recommend items that those sim-ilar users like (Breese et al., 1998). Most existingrecommender applications utilize these CF techniquesin making predictions about which items an e-serviceuser is likely to be interested in (Linden et al., 2003).Traditional CF techniques, however, can not cope atall with time specific items, like events, which typ-ically receive their ratings only after they have fin-ished (Cornelis et al., 2005). Content-based (CB)

algorithms, which consider the internal structure ofitems and recommend items similar to those a userliked in the past, or hybrid algorithms, which are acombination of CF and CB techniques, can make upfor these timely characteristics of events.

Although the details of various systems differ,CB recommender systems share in common a needfor a description of the items that may be recom-mended (Pazzani and Billsus, 2007). Many CB rec-ommender systems use relatively simple keyword ex-traction techniques to obtain characteristic propertiesfrom these descriptions. Subsequently, straightfor-ward retrieval models, such as keyword matching, areutilized to generated recommendations (Bogers andvan den Bosch, 2007). However, these techniquesare known to have been outperformed by newer tech-niques that incorporate semantic knowledge struc-tures, such as ontologies, to provide valuable domainknowledge for CB recommender systems (Middletonet al., 2003). Common-sense and domain-specificknowledge may be useful to give some meaning tothe content of items, thus helping to generate moreinformative features than ‘plain’ attributes.

Knowledge sources, such as structured semanticinformation about events, can be brought to bear indetermining similarities among events. The integra-tion of semantic similarities for items allows the sys-tem to make inferences based on the underlying rea-sons for which a user may or may not be interestedin a particular event (Mobasher et al., 2003). Ex-perimental results in literature demonstrate that theintegration of these ontological knowledge sourcesyields significant advantages in terms of learning userprofiles and recommendation accuracy in contrast tothe classical use of endogenous knowledge (extractedfrom the items themselves) (Semeraro et al., 2009).

However, acquiring such knowledge sources orsemantically structured content is not a trivial task.Moreover, content (and especially event information)from various sources is mostly annotated using a mul-titude of different metadata formats, leading to se-rious comparison difficulties for the recommender.By converting the aggregated events to our proposedevent model, we provide a uniform event structurethat can be utilized for semantic analysis. By enrich-ing the event description based on a set of knowledgesources, we generate a memory of semantic compe-tencies that can be exploited to reason about the con-tent as well as to support the user profiling and recom-mendation process. Moreover, based on our service-oriented bus-architecture as described in Section 5,we can easily integrate any existing (ontology-based)recommendation algorithm in our event distributionsystem.

To efficiently calculate the personal suggestionsbased on the adopted (ontology-based) algorithm for apotentially high number of users, we designed the rec-ommendation framework which is illustrated in Fig-ure 1. To meet the high requirements of a large rec-ommendation system, we opted for a highly scalableapproach to store and process user and event metadatacompleted with online-generated user interaction be-haviour (i.e. user feedback on the events). Moreover,due to the computational burden of most recommen-dation algorithms, the calculation of personal sugges-tions has to be realized as a distributed computationtask.

The proposed recommender framework consistsof two webservices and three small, independent ap-plications working together to collect user behaviourand metadata content from participating web sitesand to generate high quality recommendations fortheir visitors. The webservices act as a communica-tion channel between the recommendation system andthe other parts of the event distribution platform (i.e.client websites or other webservices). The Data In-sertion webservice deals with the processing of newusers, events and feedback on events, which are fedinto the recommender. Based on these data, personalevent recommendations are generated which can bequeried through the Recommendation webservice.

Each of the three applications accomplishes a sin-gle well-defined task. The first application, the DataInserter, inserts users, events and user feedback intothe storage system. Secondly, the Calculator pullsthese data from the storage system and generates per-sonal suggestions based on the implemented recom-mendation algorithm. Next, these suggestions arestored in the Recommendation Cache, making themimmediately available for retrieval by the Recommen-dation webservice. Depending on the work load, sev-eral instances of this Data Inserter and Calculatormay be created. At regular intervals, the third appli-cation, i.e. the Scheduler, generates a calculation taskwhich is split into disjunctive jobs for the Calcula-tor instances. The results of these calculation jobs aremerged and inserted into the Recommendation Cacheas an update of the personal suggestions.

Because of performance reasons, the different ap-plications are not in direct contact with each other,but use a queue service for inter-process communica-tion. Moreover, the storage system is optimized forefficiently serving two commonly-used data requests:on the one hand inserting singular user, event or feed-back information originating from the Data Insertionwebservice, and on the other hand, querying massivefeedback and event information to feed the instancesof the Calculator application.

Scalable Calculation Cloud

Storage

Clients Webservices

Data Insertion Web Service

Recommendation Web Service

Data Inserter

Calculator

Scheduler

RecommendationCache

Client Website

Other Services

Recommendationalgorithm + filters

Figure 1: The recommendation framework.

Besides the storage and calculation functionality,the recommendation framework consists of variouscontextual post-filters which take into account the per-sonal event selection criteria. These filters operate onthe list of personal event recommendations and caneliminate the suggestions which do not conform tothe personal event selection criteria. The personalevent selection criteria, which can explicitly be spec-ified by the end-user, are personal requirements re-garding the price, language, location, participation re-quirements or date of the event. E.g., events that arelocated too far from the hometown of the user are re-moved from the suggestions list, since they are mean-ingless for the end-user. Accordingly, suggestionswhich completely fulfil all the personal requirementsare favoured during the generation of the recommen-dation list. Through the proposed event ontology, asdiscussed in Section 3, and the enrichment process,as explained in Section 2 event suggestions can eas-ily be verified against the stated event requirementsbefore distribution.

5 ARCHITECTURE

Figure 2 gives a schematic overview of the infras-tructure of the complete event distribution platform,a loosely coupled bus-architecture consisting of 4components: aggregation, enrichment, recommenda-tion, and distribution. This architecture supports theflow of personalized, enriched cultural activities andconnects the various functional components of oursystem. An enterprise service bus (ESB) providesan open, standards-based connectivity infrastructurefor this Service-Oriented Architecture (SOA)12. TheSOA enables flexible connectivity of applications orresources by representing every application or re-source as a service with a standardized interface. Thisflexibility allows new and existing applications to

12http://opengroup.org/projects/soa/

be easily and quickly combined to address changingbusiness needs.

In the context of the event distribution system,this means that the different components that com-pose the general architecture are modelled as sepa-rate services where the SOA allows them to exchangedata with one another as they participate in businessprocesses. The main advantage of this SOA is to ob-tain a loose coupling between the different modulesof the system in which services can be very heteroge-neous. The implementation of these services is lan-guage and technology independent; only communi-cations must be agreed on. The high flexibility sup-ported by this technology allows an easy integrationof new sources (e.g. aggregation sources or distribu-tion targets) and remove or modify existing sourceswithout interrupting the working system. In addition,using a standardized technology provides also the ad-vantage of reusable existing middleware services. Ex-isting implementations contain many pre-built com-ponents which can be reused with a minimal effort.Existing components are available in the domain oflogic and orchestration, databases and data manipu-lation, basic interfacing, security, logging, etc. Al-though an advanced SOA setup requires some addi-tional start-up costs and a slight increase of complex-ity, it provides the freedom to expand and modify theactive setup at any time while requiring only a smallamount of effort.

The ESB technology we used for this project isthe Open Enterprise Service Bus13. OpenESB is anopen-source project with the aim of building an En-terprise Service Bus that provides a flexible and ex-tensible platform on which to build SOA and applica-tion integration solutions. Pulling event informationfrom the web is triggered by a scheduler. On prede-fined times, the scheduler sends a message to the inputservices to check the input sources for new content.Orchestration between services is handled by a work-flow engine, which is integrated in the service bus ar-chitecture and supports the execution of WS-BPELprocesses (Business Process Execution Language forWeb Services14). WS-BPEL is built on top of XMLspecifications and provides a language for the spec-ification of business processes and business interac-tion protocols. An executable WS-BPEL process isdefined by a control flow that consists of a combi-nation of basic and structured activities. WS-BPEL(and SOA in general) supports the Simple Object Ac-cess Protocol (SOAP), which is a specification for ex-changing structured information between services.

13https://open-esb.dev.java.net/

14http://docs.oasis-open.org/wsbpel/

Figure 2: The service bus architecture.

6 CONCLUSIONS

Online event-data from various providers may emergeas a dynamic mix of many different types of re-sources and facilities. This multiplicity of technolo-gies and metadata descriptions requires a uniformevent model and dynamic event distribution archi-tecture. In this paper, we proposed an event modelbased on the EventsML-G2 standard and a service-oriented bus architecture for event recommender anddistribution systems. Aggregation, enrichment, rec-ommendation and distribution are supported throughservices, which can be created and modified dy-namically to suit the current needs. This physicalloose-coupling provides scalability advantages suchas high-availability, fault-tolerance, and load balanc-ing. The combination of the semantic event modeland service-oriented architecture allows the applica-tion of an exchangeable content-based recommenda-tion algorithm. Enriching the event metadata enablesthe incorporation of semantic knowledge structuresinto the recommendation algorithm.

ACKNOWLEDGEMENTS

The research activities that have been described in thispaper were funded by Ghent University, K.U. Leuven,VUB, VRT-medialab, Interdisciplinary Institute forBroadband Technology (IBBT) through the CUPIDproject (50% co-funded by industrial partners), the In-stitute for the Promotion of Innovation by Science andTechnology in Flanders (IWT), the Fund for Scien-tific Research-Flanders (FWO-Flanders), and the Eu-ropean Union.

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