Adaptive Music News Recommendations based on Large Semantic Datasets
|Autor||Till Plumbaum, Andreas Lommatzsch, Stefan Rudnitzki, Ernesto William De Luca, Holger Düwiger, Sahin Albayrak|
|Quelle||WOMRAD 2010 - Workshop on Music Recommendation and Discovery, colocated with ACM RecSys 2010|
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We present our recent work on recommending music news articles to users based on their music preferences and large scale semantic datasets. In today's online world, people are overwhelmed with the amount of available information. Therefore, we exploit the fact that semantically linked and structured information becomes more and more available driven by a strong research community. Our solution combines these semantic, encyclopedic knowledge sources and a large news article dataset. In a first step we compute semantically related entities of interest, like similar artists or genres, based on a user model using graph-based algorithms. In a second step, we utilize these entities to compute best matching news article recommendations.