ASSOCIATION RULE LEARNING

1-Data Preprocessing

2-Preparing the ARL Data Structure (Invoice-Product Matrix)

3-Issuing Association Rules

4-Preparing the Script of the Study

5-Making Product Recommendations to Users in the Basket Stage

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CONTENT BASED RECOMMENDATION

1-Creation of TF-IDF Matrix

2-Creating the Cosine Similarity Matrix

3-Making Suggestions Based on Similarities

4-Preparation of Working Script

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MODEL-BASED COLLABORATIVE FILTERING-MATRIX FACTORIZATION

1-Preparation of Data Set

2-Modelling

3-Model Tuning

4-Final Model and Prediction

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ITEM-BASED COLLABORATIVE FILTERING

1-Preparation of Data Set

2-Creating User Movie Df

3-Making Item-Based Movie Suggestions

4-Preparation of Working Script

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USER-BASED COLLABORATIVE FILTERING

1-Preparation of Data Set

2-Determining the Movies Watched by the User to Make Recommendations

3-Accessing the Data and IDs of Other Users Watching the Same Movies

4-Determining the Users with the Most Similar Behavior to the User to be Recommended

5-Calculation of Weighted Average Recommendation Score

6-Functionalization of the Work