Meta-Prod2Vec - Product Embeddings Using Side-Information for Recommendation
This work addresses recommendation tasks in applications like content recommendation and ad targeting, but it is incremental as it builds on existing embedding methods.
The paper tackles the problem of computing item similarities for recommendation by leveraging item metadata as side information to regularize embeddings, resulting in improved performance on a music recommendation dataset.
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.