Meta-Prod2Vec - Product Embeddings Using Side-Information for Recommendation

TL;DR

Meta-Prod2vec is a new method that uses item metadata as side information to improve item similarity embeddings for recommendation tasks, leading to better performance.

Contribution

It introduces a novel embedding approach that incorporates item metadata as regularization, enhancing recommendation accuracy over existing methods.

Findings

Improved recommendation performance on a music dataset.

Effective use of item metadata as side information.

Demonstrated benefits of regularized embeddings.

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.