Metadata Embeddings for User and Item Cold-start RecommendationsMetadata Embeddings for User and Item Cold-start RecommendationsKula, Maciej2015
Paper summarymartinthomaThe 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)