In the world of recommender systems, the two most commonly used filtering methods are the collaborative approach and the content based methods. The boilerplate code for both methods is given.Recommender systems are most commonly implemented in streaming platforms so the code uses the example of a movie recommender system. These codes provides a genereal skeletal structure that can be followed while dealing with recommendation systems. However, it is important to note that the code has to be modified according the problem statement you have choosen. Furthermore, it is recommended to include a function that measures the accuracy of your system (RMSE, MAE etc) (Suggestion : Perform extensive exploratory data analysis to achieve the best possible results and learn more about your data). The accuracy is highly dataset dependent ( Ex : An RMSE of 0.9 may be bad for one dataset but fairly good on another, the dataset will be taken into account while judging). Note : A recommender system should not have just the recommender's code. It works best when it is employed in conjunction with other facets. Ex : Integrating your Recommendation system with web development and using various data security techniques to ensure data is not tampered with. This creates a full fledged application with a lot of sccope for novelty and innovation. A lot of time should be spent researching ways you can integrate all these components, choosing data security techniques (cryptographic, blockchain related etc) and so on so that the apllication can be the best possible version of itself.
For general introduction to what collaborative filtering and content based filtering are : https://youtu.be/j4CSaxG7DrI (no restrictions on the type of filtering method to be used)