For this project I analyze the interactions that users have with articles on the IBM Watson Studio platform, and make recommendations to them about new articles I think they will like.
My project is divided into the following tasks:
I. Exploratory Data Analysis
Find out the distribution of articles a user interacts within the dataset and provide a visual and descriptive statistics.
II. Rank Based Recommendations
Provide two functions to get n top articles names and n top articles ids.
III. User-User Based Collaborative Filtering
Function create_user_item_matrix: reformat the df dataframe to be shaped with users as the rows and articles as the columns.
- Each user should only appear in each row once.
- Each article should only show up in one column.
- If a user has interacted with an article, then place a 1 where the user-row meets for that article-column. It does not matter how many times a user has interacted with the article, all entries where a user has interacted with an article should be a 1.
- If a user has not interacted with an item, then place a zero where the user-row meets for that article-column
V. Matrix Factorization
Build use matrix factorization to make article recommendations to the users on the IBM Watson Studio platform