Metadata Embeddings for User and Item Cold-start Recommendations Metadata Embeddings for User and Item Cold-start Recommendations
Paper summary The idea is to combine collaborative filtering with content-based recommenders to mitigate the user and item coldstart problems. The author distinguishes between positive and negative interactions. The representation of a user and of items is the sum of all their latent representations. This sounds similar to "**Asymmetric factor models**" as described in [the BellKor Netflix price solution](https://www.netflixprize.com/assets/ProgressPrize2007_KorBell.pdf). **The key idea is to encode the latent user (or item) vector as a sum of latent attribute vectors.** Adagrad / asynchronous stochastic gradient descent was used for optimization. ## See also * [Code on GitHub](https://lyst.github.io/lightfm/docs/index.html#) * [Paper on ArXiv](https://arxiv.org/pdf/1507.08439.pdf)
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Metadata Embeddings for User and Item Cold-start Recommendations
Kula, Maciej
arXiv e-Print archive - 2015 via Local Bibsonomy
Keywords: dblp


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Summary by Martin Thoma 3 weeks ago
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