2016 MetaProd2VecProductEmbeddingsUs

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2017

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Abstract

We propose Meta-Prod2vec, a novel method to compute item similarities for recommendation that leverages existing item metadata. Such scenarios are frequently encountered in applications such as content recommendation, ad targeting and web search. Our method leverages past user interactions with items and their attributes to compute low-dimensional embeddings of items. Specifically, the item metadata is injected into the model as side information to regularize the item embeddings. We show that the new item representations lead to better performance on recommendation tasks on an open music dataset.

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 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2016 MetaProd2VecProductEmbeddingsUsFlavian Vasile
Elena Smirnova
Alexis Conneau		Meta-Prod2Vec: Product Embeddings Using Side-Information for Recommendation10.1145/2959100.29591602016
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