  Complementary products recommendation is an important problem in e-commerce.
Such recommendations increase the average order price and the number of
products in baskets. Complementary products are typically inferred from basket
data. In this study, we propose the BB2vec model. The BB2vec model learns
vector representations of products by analyzing jointly two types of data -
Baskets and Browsing sessions (visiting web pages of products). These vector
representations are used for making complementary products recommendation. The
proposed model alleviates the cold start problem by delivering better
recommendations for products having few or no purchases. We show that the
BB2vec model has better performance than other models which use only basket
data.
