Automatic news recommendations via aggregated profiling

Today, people have only limited, valuable leisure time at their hands which they want to fill in as good as possible according to their own interests, whereas broadcasters want to produce and distribute news items as fast and targeted as possible. These (developing) news stories can be characterised as dynamic, chained, and distributed events in addition to which it is important to aggregate, link, enrich, recommend, and distribute these news event items as targeted as possible to the individual, interested user. In this paper, we show how personalised recommendation and distribution of news events, described using an RDF/OWL representation of the NewsML-G2 standard, can be enabled by automatically categorising and enriching news events metadata via smart indexing and linked open datasets available on the web of data. The recommendations-based on a global, aggregated profile, which also takes into account the (dis)likings of peer friends-are finally fed to the user via a personalised RSS feed. As such, the ultimate goal is to provide an open, user-friendly recommendation platform that harnesses the end-user with a tool to access useful news event information that goes beyond basic information retrieval. At the same time, we provide the (inter)national community with standardised mechanisms to describe/distribute news event and profile information

Unifying and targeting cultural activities via events modelling and profiling

Today, people have a lot of spare time at their hands which they want to fill in according to their interests, whereas cultural temples are trying to attract interested communities to their carefully planned cultural programs. These cultural activities can be characterised as dynamic and distributed events in addition to which it is important to aggregate, enrich, recommend, and distribute these event items as targeted as possible. In this paper, we show how personalised recommendation and distribution of events, described using an RDF/OWL representation of the EventsML-G2 standard, can be enabled by automatically categorising and enriching events metadata via smart indexing and open linked datasets available on the web of data. As such, the ultimate goal of the CUPID-project is to provide an open, userfriendly platform that harnesses the end-user with a tool to access useful event information that goes beyond basic information retrieval. At the same time, we provide the (inter)national cultural community with standardised mechanisms to describe/distribute event and profile information.status: publishe

Unifying and targeting cultural activities via events modelling and profiling

Today, people have only limited, valuable spare time at their hands which they want to fill in as good as possible according to their interests. At the same time, cultural institutions are trying to attract interested communities to their carefully planned cultural programs. To distribute these cultural events to the right people, we developed a framework that will aggregate, enrich, recommend and distribute these events as targeted as possible. The aggregated events are published as Linked Open Data using an RDF/OWL representation of the EventsML-G2 standard. These event items are categorised and enriched via smart indexing and linked open datasets available on the Web of data. For recommending the events to the end-user, a global profile of the end-user is automatically constructed by aggregating his profile information from all user communities the user trusts and is registered to. This way, the recommendations take profile information into account from different communities, which has a detrimental effect on the recommendations. As such, the ultimate goal is to provide an open, user-friendly recommendation platform that harnesses the end-user with a tool to access useful event information that goes beyond basic information retrieval. At the same time, we provide the (inter)national cultural community with standardised mechanisms to describe/distribute event and profile information

Unifying and targeting cultural activities via events modelling and profiling

Nowadays, most people have limited leisure time and the offer of (cultural) activities to spend this time is enormous. Consequently, picking the most appropriate events becomes increasingly difficult for end-users. This complexity of choice reinforces the necessity of filtering systems that assist users in finding and selecting relevant events. Whereas traditional filtering tools enable e.g. the use of keyword-based or filtered searches, innovative recommender systems draw on user ratings, preferences, and metadata describing the events. Existing collaborative recommendation techniques, developed for suggesting web-shop products or audio-visual content, have difficulties with sparse rating data and can not cope at all with event-specific restrictions like availability, time, and location. Moreover, aggregating, enriching, and distributing these events are additional requisites for an optimal communication channel. In this paper, we propose a highly-scalable event recommendation platform which considers event-specific characteristics. Personal suggestions are generated by an advanced collaborative filtering algorithm, which is more robust on sparse data by extending user profiles with presumable future consumptions. The events, which are described using an RDF/OWL representation of the EventsML-G2 standard, are categorized and enriched via smart indexing and open linked data sets. This metadata model enables additional content-based filters, which consider event-specific characteristics, on the recommendation list. The integration of these different functionalities is realized by a scalable and extendable bus architecture. Finally, focus group conversations were organized with external experts, cultural mediators, and potential end-users to evaluate the event distribution platform and investigate the possible added value of recommendations for cultural participation.status: publishe

A reverse signaling pathway downstream of Sema4A controls cell migration via Scrib

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