Heterogeneous Embedding for Subjective Artist Similarity
نویسندگان
چکیده
We describe an artist recommendation system which integrates several heterogeneous data sources to form a holistic similarity space. Using social, semantic, and acoustic features, we learn a low-dimensional feature transformation which is optimized to reproduce human-derived measurements of subjective similarity between artists. By producing low-dimensional representations of artists, our system is suitable for visualization and recommendation tasks.
منابع مشابه
The Quest for Ground Truth in Musical Artist Similarity
It would be interesting and valuable to devise an automatic measure of the similarity between two musicians based only on an analysis of their recordings. To develop such a measure, however, presupposes some ‘ground truth’ training data describing the actual similarity between certain pairs of artists that constitute the desired output of the measure. Since artist similarity is wholly subjectiv...
متن کاملLink Prediction using Network Embedding based on Global Similarity
Background: The link prediction issue is one of the most widely used problems in complex network analysis. Link prediction requires knowing the background of previous link connections and combining them with available information. The link prediction local approaches with node structure objectives are fast in case of speed but are not accurate enough. On the other hand, the global link predicti...
متن کاملThe World of Music: User Ratings; Spectral and Spherical Embeddings; Map Projections
In this paper we present an algorithm for layout and visualization of music collections based on similarities between musical artists. The core of the algorithm consists of a non-linear low dimensional embedding of a similarity graph constrained to the surface of a hyper-sphere. This approach effectively uses additional dimensions in the embedding. We derive the algorithm using a simple energy ...
متن کاملHeterogeneous Information Network Embedding for Meta Path based Proximity
A network embedding is a representation of a large graph in a lowdimensional space, where vertices are modeled as vectors. The objective of a good embedding is to preserve the proximity (i.e., similarity) between vertices in the original graph. This way, typical search and mining methods (e.g., similarity search, kNN retrieval, classification, clustering) can be applied in the embedded space wi...
متن کاملPredicting genre labels for artist using FreeDB
This paper explores the value of FreeDB as a source of genre and music similarity information. FreeDB is a public, dynamic, uncurated database for identifying and labeling CDs with album, song, artist and genre information. One quality of FreeDB is that there is high variance in, e.g., the genre labels assigned to a particular disc. We investigate here the ability to use these genre labels to p...
متن کامل