Skip to content

pariskimchi/Recommendation_With_IBM

Repository files navigation

Recommendation_With_IBM

Introduction

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 you think they will like. Below you can see an example of what the dashboard could look like displaying articles on the IBM Watson Platform.

My project is divided into 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

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 matrix factorization to make article recommendations to the users on the IBM Watson Studio platform using user_item matrix .

About

No description, website, or topics provided.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published