With this, we come to the end of this chapter, as well as the main part of the book. In this book, we learned the following:
- We were introduced to the world of recommender systems. We defined the recommendation problem mathematically and discussed the various types of recommendation engines that exist, as well as their advantages and disadvantages.
- We then learned to perform data wrangling with the pandas library and familiarized ourselves with two of pandas, most powerful data structures: the series and the DataFrame.
- With our newly found data wrangling techniques, we proceeded to build an IMDB Top 250 clone. We then improved on this model to build a knowledge-based recommender that took into account the recommended movies' genre, duration, and year of release.
- Next, we learned how to build content-based recommenders using plot lines and subsequently more sophisticated...
