Exam Professional Machine Learning Engineer topic 1 question 121 discussion

When no data: recommend similar videos to the users. D is too complex.

D is overkill

My choice is B. This is because both B and D have the same goal (recommendation based on content), but option B is simpler for this initial context.

This is because you do not have any historical user event data, so you cannot use a collaborative filtering approach to build a recommender system. However, you can still use simple heuristics based on content metadata to recommend similar videos to users. For example, you could recommend videos that are in the same genre, have the same release date, or are from the same country. You should also start collecting user event data as soon as possible. This data will be valuable for training a recommender model in the future. Option D is a more complex approach that would require you to have more expertise in machine learning.(FOR THE FIRST VERSION OF THE PRODUCT)

B: https://developers.google.com/machine-learning/guides/rules-of-ml

B since we can't use othe encoded data to test on some other system

Went with D

https://cloud.google.com/blog/topics/developers-practitioners/meet-ais-multitool-vector-embeddings Option D is about creating clusters based on the content metadata and using that to provide recos to users

Key is the mention “first version of product”

It is possible to create a recommendation system just using metadata information, like in: https://developers.google.com/machine-learning/crash-course/embeddings/categorical-input-data One of the initial problems of recommender systems is precisely the lack of data for collaborative recommendation. However, this does not prevent other recommendation algorithms, for example, those that use content suggestion.

Since you do not have any historical user event data, options C and D are not suitable. In this scenario, it is better to start with a simpler approach, so options A and B are the most suitable. However, option B is preferred because it uses some logic based on content metadata to provide recommendations, which may be more personalized and relevant than presenting videos in alphabetical order. Additionally, collecting user event data from the beginning will help improve the recommendation system in the future.

ans B, you need something easier to implement

https://developers.google.com/machine-learning/crash-course/embeddings/categorical-input-data

B - https://developers.google.com/machine-learning/guides/rules-of-ml

because no user event data, the pretrain model won't help