I’m working on a Machine Learning Job Recommendation System and I’m considering using an ensemble learning approach. The dataset I’m using is comprehensive and includes a variety of attributes such as job title, job description, salary, location, and company details. It contains a mix of numerical, categorical, and text data.
I’m planning to use a hybrid approach that combines several models and techniques in order to improve its performance:
Collaborative Filtering or Matrix Factorization to capture the interactions between users and job postings.
Neural Networks to process complex data types like text in job descriptions.
Decision Trees or Random Forests for their interpretability and ability to handle a mix of numerical and categorical data.
I’m looking for advice on which ensemble method would be best suited for this task. I want the model to filter well, be flexible with data types, have good performance at training and time, and offer interpretability. Thank you all & have a good day!

I haven’t started yet, but i will consider giving a shot to any reasonable approach!