Corpus ID: 8671641

Item-Item Music Recommendations With Side Information

@article{Demir2017ItemItemMR,
  title={Item-Item Music Recommendations With Side Information},
  author={{\"O}zg{\"u}r Demir and A. R. Yakushev and Rany Keddo and Ursula Kallio},
  journal={ArXiv},
  year={2017},
  volume={abs/1706.00218}
}
Online music services have tens of millions of tracks. The content itself is broad and covers various musical genres as well as non-musical audio content such as radio plays and podcasts. The sheer scale and diversity of content makes it difficult for a user to find relevant tracks. Relevant recommendations are therefore crucial for a good user experience. Here we present a method to compute track-track similarities using collaborative filtering signals with side information. On a data set from… Expand
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References

SHOWING 1-10 OF 14 REFERENCES
Item cold-start recommendations: learning local collective embeddings
TLDR
This work proposes to learn Local Collective Embeddings: a matrix factorization that exploits items' properties and past user preferences while enforcing the manifold structure exhibited by the collective embeddings and presents a learning algorithm based on multiplicative update rules that is efficient and easy to implement. Expand
Music Genre Classification via Compressive Sampling
TLDR
A CS-based classifier for music genre classification, with two sets of features, including short-time and long-time features of audio music, that generates a compact signature to achieve a significant reduction in the dimensionality of the audio music signals. Expand
Collaborative Filtering for Implicit Feedback Datasets
TLDR
This work identifies unique properties of implicit feedback datasets and proposes treating the data as indication of positive and negative preference associated with vastly varying confidence levels, which leads to a factor model which is especially tailored for implicit feedback recommenders. Expand
The YouTube video recommendation system
TLDR
The video recommendation system in use at YouTube, the world's most popular online video community, is discussed, with details on the experimentation and evaluation framework used to test and tune new algorithms. Expand
Web-Scale Multimedia Analysis: Does Content Matter?
TLDR
It's useful to look at several examples where content has lost out to other forms of data, including the worlds of music, movies, and images, where metadata about the content proves to be more useful. Expand
Features for Content-Based Audio Retrieval
TLDR
The goal of this chapter is to review latest research in the context of audio feature extraction and to give an application-independent overview of the most important existing techniques, and to propose a novel taxonomy for the organization of audio features. Expand
Swivel: Improving Embeddings by Noticing What's Missing
We present Submatrix-wise Vector Embedding Learner (Swivel), a method for generating low-dimensional feature embeddings from a feature co-occurrence matrix. Swivel performs approximate factorizationExpand
Evaluating similarity measures: a large-scale study in the orkut social network
TLDR
An extensive empirical comparison of six distinct measures of similarity for recommending online communities to members of the Orkut social network is presented, determining the usefulness of the different recommendations by actually measuring users' propensity to visit and join recommended communities. Expand
Factorization Machines
  • Steffen Rendle
  • Mathematics, Computer Science
  • 2010 IEEE International Conference on Data Mining
  • 2010
TLDR
Factorization Machines (FM) are introduced which are a new model class that combines the advantages of Support Vector Machines (SVM) with factorization models and can mimic these models just by specifying the input data (i.e. the feature vectors). Expand
GloVe: Global Vectors for Word Representation
TLDR
A new global logbilinear regression model that combines the advantages of the two major model families in the literature: global matrix factorization and local context window methods and produces a vector space with meaningful substructure. Expand
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