Tensor-based Complementary Product Recommendation

Negin Entezari, Evangelos E. Papalexakis, Haixun Wang, Sharath Rao, Shishir Kumar Prasad

Abstract: In recent years, online grocery shopping has become very popular, and platforms such as Instacart, Amazon Fresh, Shipt, and Walmart Grocery have attracted millions of customers. To satisfy the customers’ needs, it is vital to provide relevant personalized recommendations and ease the customers’ shopping experience. In this paper, we propose a tensor-based method that utilizes a three-mode tensor to represent product-to-product relations for users and applies tensor decomposition techniques to jointly learn user and product embeddings that can be used to infer within-basket recommendations. Products co-purchased in a single transaction are modeled in the form of a tensor. Then, we leverage RESCAL tensor decomposition technique to capture the latent factors that reveal the inherent user and product interactions. On the Instacart dataset, our proposed tensor-based method achieves a recall@10 of 0.192, whereas recall@10 for triple2vec, which is the state-of-the-art, is 0.149.