hima888 / article-recommendations-with-ibm

Article-Recommendations-with-IBM

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.

we will go through 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