https://github.com/londheshubham153/recommendations-with-ibm
To analyse the interactions that users have with articles on the IBM Watson Studio platform, and make recommendations to them about new articles you think they will like.
https://github.com/londheshubham153/recommendations-with-ibm
Last synced: 2 months ago
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To analyse the interactions that users have with articles on the IBM Watson Studio platform, and make recommendations to them about new articles you think they will like.
- Host: GitHub
- URL: https://github.com/londheshubham153/recommendations-with-ibm
- Owner: LondheShubham153
- Created: 2020-05-15T05:56:10.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2020-05-15T11:50:53.000Z (almost 5 years ago)
- Last Synced: 2025-01-05T19:09:52.846Z (4 months ago)
- Language: HTML
- Size: 3.99 MB
- Stars: 2
- Watchers: 3
- Forks: 2
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# 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.
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