Inferring Complementary Products from Baskets and Browsing Sessions

Abstract

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.

arXiv commentWorkshop on Intelligent Recommender Systems by Knowledge Transfer and Learning (RecSysKTL'18)

References in corpus (5)

• Efficient Estimation of Word Representations in Vector Space
• BPR: Bayesian Personalized Ranking from Implicit Feedback
• Inferring Networks of Substitutable and Complementary Products
• Exponential Family Embeddings
• Hierarchical Neural Language Models for Joint Representation of Streaming Documents and their Content

Cited by in corpus (3)

• Semi-supervised Adversarial Learning for Complementary Item Recommendation
• Identifying Substitute and Complementary Products for Assortment Optimization with Cleora Embeddings
• Session-based Complementary Fashion Recommendations