Inferring Complementary Products from Baskets and Browsing Sessions
This addresses the cold start problem in e-commerce recommendations, potentially increasing sales, but it is incremental as it builds on existing embedding methods by incorporating additional data types.
The paper tackled the problem of complementary product recommendation in e-commerce by proposing the BB2vec model, which jointly analyzes basket and browsing session data to learn product vector representations, resulting in better performance for products with few or no purchases compared to models using only basket data.
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.