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.

Causation is gradually paid more attention to in industry as compared with correlation statement, it straightly targets on answering what-if questions, which generally delivers deeper and more insightful conclusions. Therefore, causal inference is naturally called. Mainly targeting on modeling counter-factual relationship that is usually not directly observable, causal inference has various of challenges on both problem setup and modeling side, which makes it a more complex topic than regular supervised learning task. As one of the heated discussed specific causal inference problems, conditional average treatment effect (CATE), or heterogeneous treatment effect (HTE), estimation model serves as a powerful tool in many applications, like personalized medicine and a series of uplift problems from user segmentation to ads budget optimization. Recently, several new CATE methods were proposed and we would like to do a short survey from the perspective of forest-based model to cover both meta-learners that could take random forest as base learner and forest-based specific CATE models. In total, we discussed 7 meta-learners and 5 forest-based specific models. We empirically evaluate these models with both synthetic data and real dataset.