In this chapter, we covered the concepts of the recommendation system using the user-based and item-based methodologies. In the process, you learned techniques to convert the data into a format ready to be consumed by the recommendation algorithm and to compute the similarity score using both the cosine similarity and the correlation methods. Additionally, the methodology to compute is explained, empowering the reader to try similar methodology and finally, arrive at the final recommendation list. In the end, we touched on the various techniques that can be used to improve the accuracy of our recommendation engine.

The recommendation engine is a popular technique used across multiple industries and has also proved in bringing monetary benefit to the business. For example, the e-commerce industry uses this to provide recommendations to the users on the products that they might be interested in buying based on their behavior as well as other similar users' behavior, thereby increasing...