Meta-Prod2Vec - Product Embeddings Using Side-Information for Recommendation
Information Retrieval
2016-07-26 v1 Artificial Intelligence
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 in- jected into the model as side information to regularize the item embeddings. We show that the new item representa- tions lead to better performance on recommendation tasks on an open music dataset.
Cite
@article{arxiv.1607.07326,
title = {Meta-Prod2Vec - Product Embeddings Using Side-Information for Recommendation},
author = {Flavian Vasile and Elena Smirnova and Alexis Conneau},
journal= {arXiv preprint arXiv:1607.07326},
year = {2016}
}