Sound and Music Recommendation with Knowledge Graphs

By Sergio Oramas, Vito Claudio Ostuni, Tommaso Di Noia, Xavier Serra, Eugenio Di Sciascio. 2016

152 citations recorded by the source corpus.

The Web has moved, slowly but steadily, from a collection of documents towards a collection of structured data. Knowledge graphs have then emerged as a way of representing the knowledge encoded in such data as well as a tool to reason on them in order to extract new and implicit information. Knowledge graphs are currently used, for example, to explain search results, to explore knowledge spaces, to semantically enrich textual documents, or to feed knowledge-intensive applications such as recommender systems. In this work, we describe how to create and exploit a knowledge graph to supply a hybrid recommendation engine with information that builds on top of a collections of documents describing musical and sound items. Tags and textual descriptions are exploited to extract and link entities to external graphs such as WordNet and DBpedia, which are in turn used to semantically enrich the initial data. By means of the knowledge graph we build, recommendations are computed using a feature combination hybrid approach. Two explicit graph feature mappings are formulated to obtain meaningful item feature representations able to catch the knowledge embedded in the graph. Those content features are further combined with additional collaborative information deriving from implicit user feedback. An extensive evaluation on historical data is performed over two different datasets: a dataset of sounds composed of tags, textual descriptions, and user’s download information gathered from Freesound.org and a dataset of songs that mixes song textual descriptions with tags and user’s listening habits extracted from Songfacts.com and Last.fm, respectively. Results show significant improvements with respect to state-of-the-art collaborative algorithms in both datasets. In addition, we show how the semantic expansion of the initial descriptions helps in achieving much better recommendation quality in terms of aggregated diversity and novelty.

Subfields: Artificial Intelligence, Information Systems, Signal Processing

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Citations in the corpus

Knowledge-aware Autoencoders for Explainable Recommender Systems (2018, background): “Looking at the last mentioned method, it is worth noticing how Knowledge Graphs (KG) are recently being used in lots of applications; they freely offer a large amount of structured data which turned out to be very useful also in recommendation scenarios [4, 5, 12].”
Learning over Knowledge-Base Embeddings for Recommendation (2018, methodology): “[14] adopted knowledge graphs to produce sound and music recommendations, and Catherine et al.”
Schema-summarization in linked-data-based feature selection for recommender systems (2017, methodology): “In [18] two graph-based kernel methods for LOD-enabled”