Product matching for online retail

The aim of this project was to develop a product matching framework for online retail using Python. The primary goal of the framework was to improve the accuracy and efficiency of product matching for online retailers by identifying and recommending similar or related products to customers based on their browsing and purchase history.

To achieve this goal, we made the following assumptions:

Product data, including product descriptions and attributes, are available and can be used to compute similarity measures between products.

Customer data, including browsing and purchase history, is available and can be used to compute preferences and interests of customers.

A recommendation system based on collaborative filtering, where recommendations are generated based on the preferences and interests of similar customers, can be used to identify and recommend related products to customers.

To implement the product matching framework, we followed the following steps:

Preprocessing: We cleaned and transformed the product and customer data to prepare it for analysis. This involved removing missing or irrelevant data, standardizing attribute values, and encoding categorical variables.

Similarity measures: We computed similarity measures between products using a combination of feature-based and content-based methods. Feature-based methods compared the attributes of products, such as price, color, and size, while content-based methods used the product descriptions and other text data to compute similarity.

Collaborative filtering: We used a collaborative filtering algorithm to generate recommendations for customers based on their browsing and purchase history. The algorithm used the similarity measures between products and the preferences of similar customers to identify and recommend related products.

Evaluation: We evaluated the performance of the product matching framework using a set of metrics, including precision, recall, and coverage, to measure the accuracy and effectiveness of the recommendations.

In conclusion, we have successfully implemented a product matching framework for online retail using Python that is able to accurately and efficiently recommend similar or related products to customers based on their browsing and purchase history. The framework is flexible and can be easily customized and integrated into the product recommendation system of an online retailer.