Session based recommendation systems are highly effective in e-commerce recommendations since they take into account a buyer's most recent interactions, thereby providing timely and personalized recommendations. In this work, we outline a personalized recommendation retrieval system that leverages the user's short-term interaction history. Our proposed system consists of a deployed machine learning model that: 1. generates user embeddings leveraging their site-wide short term interaction history in real time, 2. generates multimodal item embeddings using textual and image data, 3. employs contrastive learning to map users and items into a shared embedding space. We further illustrate the usage of approximate nearest neighbour algorithm for efficient real time retrieval of items, discuss strategies for deploying and maintainin
Nagpal et al. (Tue,) studied this question.