Adaptive Music News Recommendations based on Large Semantic Datasets
Till Plumbaum, Andreas Lommatzsch, Stefan Rudnitzki · 2010
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. 1. THE MUSIC NEWS RECOMMENDER Our proposed music news recommender system supports users in finding interesting and up-to-date news articles about their favorite music artists and events. The knowledge base of the recommender system is a music dataset obtained from Freebase 1 [1] consisting of ≈400,000 artists, ≈4,600,000 tracks and albums, and ≈2,000 genres, connected by ≈1.5 million edges. This information is linked to a news corpus, provided by Neofonie GmbH, currently containing 7,200,000 news articles with 40,000 articles added on average each day. Both datasets are combined by extracting entities from the news articles using named entity recognition methods. These entities are then linked to the Freebase dataset building a large knowledge graph, modeled as an ontology. Recommendations are computed in a user-centric way by combining a user model with the knowledge graph. In the demo, we simulate the user model by entering interests in a search field. Explicit ratings will be added in the future. The recommender applies several semantic recommendation algorithms and creates an aggregated result set considering the search context and the provided user input.