# Distributed Vector Representation Of Shopping Items, The Customer And Shopping Cart To Build A Three Fold Recommendation System

## TL;DR

This paper introduces a novel three-fold recommendation system using vector embeddings of products, customer profiles, and shopping trips, leveraging exponential family embeddings to enhance personalized shopping suggestions.

## Contribution

It presents a new approach by combining product, trip, and customer embeddings for improved recommendation accuracy, a first in this domain.

## Key findings

- Built product, trip, and customer embeddings using exponential family models.
- Demonstrated the effectiveness of trip embeddings in predicting next product purchases.
- Enhanced customer profiling for targeted marketing and discounts.

## Abstract

The main idea of this paper is to represent shopping items through vectors because these vectors act as the base for building em- beddings for customers and shopping carts. Also, these vectors are input to the mathematical models that act as either a recommendation engine or help in targeting potential customers. We have used exponential family embeddings as the tool to construct two basic vectors - product embeddings and context vectors. Using the basic vectors, we build combined embeddings, trip embeddings and customer embeddings. Combined embeddings mix linguistic properties of product names with their shopping patterns. The customer embeddings establish an understand- ing of the buying pattern of customers in a group and help in building customer profile. For example a customer profile can represent customers frequently buying pet-food. Identifying such profiles can help us bring out offers and discounts. Similarly, trip embeddings are used to build trip profiles. People happen to buy similar set of products in a trip and hence their trip embeddings can be used to predict the next product they would like to buy. This is a novel technique and the first of its kind to make recommendation using product, trip and customer embeddings.