The project is divided into the following tasks:
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I. Exploratory Data Analysis
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II. Rank Based Recommendations
- To get started in building recommendations, you will first find the most popular articles simply based on the most interactions. Since there are no ratings for any of the articles, it is easy to assume the articles with the most interactions are the most popular. These are then the articles we might recommend to new users (or anyone depending on what we know about them).
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III. User-User Based Collaborative Filtering
- In order to build better recommendations for the users of IBM's platform, we could look at users that are similar in terms of the items they have interacted with. These items could then be recommended to the similar users. This would be a step in the right direction towards more personal recommendations for the users. You will implement this next.
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IV. Content Based Recommendations
- Given the amount of content available for each article, there are a number of different ways in which someone might choose to implement a content based recommendations system.
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V. Matrix Factorization
- Finally, you will complete a machine learning approach to building recommendations. Using the user-item interactions, you will build out a matrix decomposition. Using your decomposition, you will get an idea of how well you can predict new articles an individual might interact with.