Each Notebook have a case for a recommendation system. This repo includes below python notebooks:

1. Association Rule Learning Recommender (ARL Recommender)

The data set named Online Retail II is a UK-based online sale store's sales between 01/12/2009 - 09/12/2011 The product catalog of this company includes souvenirs.

DATASET {InvoiceNo StockCode Description Quantity InvoiceDate UnitPrice (Sterling) CustomerID Country}

The goal is to apply association analysis to the online retail II dataset (in this case we will check Germany as an example) and suggest products. Keywords: apriori , relational, basket, lift-support-confidence

2. Hybrid Systems User-Based/Item-Based Recommender

MovieLens Data https://grouplens.org/datasets/movielens/ Small: 100,000 ratings and 3,600 tag applications applied to 9,000 movies by 600 users. Last updated 9/2018. movies.csv : movieId, title, genres, rating.csv : userId movieId rating timeStamp

We will find first the users-based recommeded 5 top movies after that 5 more recommendations will be given by item-based recommender depends on the user's last watched movies. Key words: pearson correlation, hybrid, user-based, item-based, similar users, similar items