Recommender-Systems

Content-based and Collaborative Filtering recommendation systems and implemented a simple version of one using Python and the Pandas library.

Recommendation systems are a collection of algorithms used to recommend items to users based on information taken from the user. These systems have become ubiquitous, and can be commonly seen in online stores, movies databases and job finders.

Implemented a simpler version of Content based and Collaborative Filtering Recommender Systems using Numpy and Pandas Library. This Project was done under a MOOC course by IBM.

Advantages and Disadvantages of Content-Based Filtering

• Advantages
  • Learns user's preferences
  • Highly personalized for the user
• Disadvantages
  • Doesn't take into account what others think of the item, so low quality item recommendations might happen
  • Extracting data is not always intuitive
  • Determining what characteristics of the item the user dislikes or likes is not always obvious

Advantages and Disadvantages of Collaborative Filtering

• Advantages
  • Takes other user's ratings into consideration
  • Doesn't need to study or extract information from the recommended item
  • Adapts to the user's interests which might change over time
• Disadvantages
  • Approximation function can be slow
  • There might be a low of amount of users to approximate
  • Privacy issues when trying to learn the user's preferences