Hybrid Recommender System using LightFM

Business Objective

There are two main methods for making these suggestions: content-based and collaborative filtering. Collaborative filtering finds similarities between users to make recommendations, while content-based filtering personalizes content for each user based on their previous actions and feedback.

However, these methods struggle when there's not enough data. To address this, we'll explore a Hybrid Recommendation System, which combines both approaches.

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Data Description

The dataset used in this project contains transactional data for a UK-based online retail company that sells unique gifts for various occasions.

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Aim

Our goal is to build a Hybrid Recommendation system using different loss functions with the LightFM library.

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Tech Stack

• Language: Python
• Libraries: pandas, numpy, scipy, lightfm

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Approach

1. Import required libraries
2. Read and merge the data
3. Prepare the data
4. Split the data into training and testing sets
5. Build models
   • Model with WARP loss function
   • Model with logistic loss function
   • Model with BPR loss function
6. Combine data for the final model
7. Generate recommendations

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Modular Code

1. input: Contains the data we'll use for analysis, such as data.xlsx.

2. src: This folder holds all the code for our project, organized in a modular manner. It includes:

   • ML_pipeline
   • engine.py

   The ML_pipeline folder contains functions organized in different Python files, which are called from the engine.py file. There's also a config.ini file in the input folder, storing variables used in engine.py.

3. output: Contains our final models saved in pickle format.

4. lib: This is a reference folder that includes the original IPython notebook and reference pdfs for explanation.

5. requirements.txt: Lists all the required libraries with their respective versions. Install these libraries using the command pip install -r requirements.txt.

6. Instructions for running the code are in the readme.md file.

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Getting Started

Install all the requirements

• pip install -r requirements.txt

Run the engine.py file to execute the code

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Note

In case you face issues while installing the 'lightfm' package; Try the following two methods:

1. In your VS code, perform the following executions on your terminal window

   • Upgrade your pip with: python -m pip install --upgrade pip

   • Upgrade your wheel with: pip install --upgrade wheel

   • Upgrade your setuptools with: pip install --upgrade setuptools

   • close the terminal

   • Try installing the pacakage again.

2. In case you face; error: Microsoft Visual C++ Download and install

   • https://visualstudio.microsoft.com/visual-cpp-build-tools/

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