Client Background

Client: A leading retail firm in the USA

Industry Type: Retail

Products & Services: Retail Business, e-commerce

Organization Size: 100+

The Problem

Design and develop a product recommendation engine based on the features of products

Our Solution

Content-based product recommendation system has been created using Machine Learning Algorithm and Python.

Solution Architecture

Recommendation engine have six cases which are mentioned below:

Case 1:

  1. Description: Given an object name or PAR ID, inp_prodname recommends products with the same object type as the input and ranks them based on the number of specifications matched.
  2. Input: JSON format with the following keys: Object Name, PAR ID, Debug Information, userDef1, userDef2, userDef3.
  3. Output: JSON format with the following keys: Object Name, Object Type, PAR ID, Rank, Specifications, userDef1, userDef2, userDef3.

Case 2:

  1. Description: Given specifications and an object type, inp_custom_spec recommends products and ranks them based on the number of specifications matched.
  2. Input: JSON format with the following keys: Specifications, Object Type, userDef1, userDef2, userDef3.
  3. Output: JSON format with the following keys: Object Name, Object Type, PAR ID, Rank, userDef1, userDef2, userDef3.

Case 3:

  1. Description: Based on compatible models, inp_prodname_comp recommends products and ranks them based on the number of compatible models matched.
  2. Input: JSON format with the following keys: Object Name, PAR ID, Debug Information, userDef1, userDef2, userDef3.
  3. Output: JSON format with the following keys: Object Name, Object Type, PAR ID, Rank, Compatible Models, userDef1, userDef2, userDef3.

Case 4:

  1. Description: Based on the number of specifications entered by the user, inp_spec_num creates clusters of products with the same number of specifications.
  2. Input: JSON format with the following keys: Number of Specifications, Object Type, userDef1, userDef2, userDef3.
  3. Output: JSON format with the following keys: Cluster ID, Object Name, Object Types, PAR ID, specifications_grped, userDef1, userDef2, userDef3.

Case 5:

  1. Description: Given specification attributes and an object type, inp_spec_attr creates clusters of products with the same specifications.
  2. Input: JSON format with the following keys: Specification Attributes, Object Type, Debug Information, userDef1, userDef2, userDef3.
  3. Output: JSON format with the following keys: PAR ID, Object Name, Object Type, Cluster ID, Specifications, userDef1, userDef2, userDef3.

Case 6:

  1. Description: Based on the object name or PAR ID entered by the user, inp_prodname_model creates clusters of products with similar specifications.
  2. Input: JSON format with the following keys: Object Name, PAR ID, Debug Information, userDef1, userDef2, userDef3.
  3. Output: JSON format with the following keys: Object Name, Object Types, PAR ID, Specifications, Rank, userDef1, userDef2, userDef3.

The APIs for all the above cases have been created

Deliverables

The code of the recommendation engine and its API is been delivered.

Tools used

Python, Postman

Language/techniques used

Python, Machine Learning, Flask API, Pandas

Models used

Affinity Propagation is a clustering algorithm that does not require a predefined number of clusters. It is used to group products based on their similarities.

Skills used

Python, Logical Reasoning, Machine Learning, Data Engineering.

What are the technical Challenges Faced during Project Execution

  1. Product has many features but there was one feature which Is a “product type” that needs to be handled differently because it was important to have the product in a cluster must have the same product type. 
  2. Some cases can’t be solved with machine learning algorithms.

How the Technical Challenges were Solved

  1. Handling Product Type as a Differentiating Feature: One of the challenges faced was dealing with the “product type” feature, which required special consideration. It was crucial to ensure that products with the same type were grouped together in the clustering or recommendation algorithm. This required developing a specific approach to address the uniqueness of the product type feature and incorporate it effectively into the recommendation system. Custom modifications and additional preprocessing steps were likely needed to accommodate this requirement and ensure accurate clustering based on product type.
  2. Limitations of Machine Learning Algorithms: While machine learning algorithms are powerful tools for recommendation systems, there are cases where they may not be sufficient to solve certain challenges. During the project, it was likely discovered that some complex scenarios couldn’t be adequately addressed using traditional machine learning algorithms alone. To overcome this, alternative techniques and approaches beyond the scope of standard algorithms needed to be explored. This might involve incorporating domain-specific rules, utilizing other data analysis methods, or considering hybrid models that combine machine learning with expert knowledge to overcome the limitations and improve the recommendation system’s performance.

Business Impact

This recommendation engine can significantly enhance customers’ shopping experience by increasing the likelihood of them discovering products that perfectly align with their preferences. This personalized approach not only saves them valuable time and effort in searching for relevant items but also ensures that their unique needs and desires are met. As a result, customers are more likely to make purchases, leading to increased sales and revenue for the business.

Moreover, this recommendation engine plays a crucial role in improving customer satisfaction and fostering long-term loyalty. By suggesting products based on individual preferences and specific features, customers feel understood and valued. This tailored experience enhances their overall satisfaction, making them more inclined to return to the business for future purchases. Additionally, satisfied customers are more likely to spread positive word-of-mouth, attracting new customers and expanding their customer base.

 Project Snapshots

Summarize

Summarized: https://blackcoffer.com/

This project was done by the Blackcoffer Team, a Global IT Consulting firm.

Contact Details

This solution was designed and developed by Blackcoffer Team
Here are my contact details:
Firm Name: Blackcoffer Pvt. Ltd.
Firm Website: www.blackcoffer.com
Firm Address: 4/2, E-Extension, Shaym Vihar Phase 1, New Delhi 110043
Email: ajay@blackcoffer.com
Skype: asbidyarthy
WhatsApp: +91 9717367468
Telegram: @asbidyarthy

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