September 17th, 2016

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

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.

September 6th, 2016

Product Recommendation at Criteo

As we said time and again, performance is everything. The performance of our ads depends a lot on the content we put in them. We’ve found that personalized product selection makes for better ads: people tend to click more and buy more when we display content that’s tailored for them specifically. We’re able to gather a lot of browsing data on our merchant websites, and we have a fantastic prediction team focusing on scaling common regression algorithms for this volume of data. But we have billions of products and billions of users, so exploring the whole realm of display possibilities is untractable. Keeping the data up to date is also of paramount importance. Our merchants’ products change daily, and our users always visit new pages. So how can we display relevant ads taking into account information dating from several months as well as under a minute?