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Collaborative Filtering Recommender System Project - Comparison of different model based and memory based methods to build recommendation system using collaborative filtering.
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Business Objective
We often ask our friends with the same tastes to recommend products to use, movies to watch, and whatnot. We trust the recommendations of friends whose taste is similar to ours. Collaborative filtering does the same job. Collaborative filtering is primarily focused on finding similarities between users and mutually encouraging user preferences. Collaborative filtering is the predictive process behind the recommendation engine. The recommendation engine analyzes information about users with similar tastes and assesses the target person's likelihood of enjoying something. Collaborative filtering uses various algorithms to filter data from user reviews and provide personalized recommendations to users with similar settings. Collaborative filtering uses interactions and data collected from other users to filter information.
As a part of a series of Recommender system projects, this project covers Recommendations using a wide variety of Collaborative Filtering algorithms in Python. If you haven’t already visited, here is the previous project of the series Recommender System Machine Learning Project for Beginners-3
Data Description
This is a transactional data set containing the transactions for a UK-based non-store online retail. The company mainly sells unique all-occasion gifts.
Aim
To build a Recommender system using various Model-Based and Memory-based collaborative filtering techniques
Tech Stack
Language: Python 3.8.10
Libraries: sklearn, surprise, pandas, matplotlib, scipy, numpy, pickle
Approach:
Data Description
Data cleaning
Memory-based approach
User-to-User collaborative recommendation
Item-to-Item collaborative recommendation
Models:
KNN model
Non-negative Matrix Factorization (NMF)
Co-Clustering model
Evaluation metrics:
Root mean square error
Mean absolute error
Cross-validation score
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