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Answer: Launch the product without machine learning. Use simple heuristics based on content metadata to recommend similar videos to users, and start collecting user event data so you can develop a recommender model in the future.
The correct approach to building the recommendation system for the first version of the product is to use simple heuristics based on content metadata to recommend similar videos to users. This is because there is no historical user event data available, making it impossible to use collaborative filtering methods. By utilizing the available metadata, you can implement a straightforward recommendation system that can be improved over time as user event data starts to be collected. Option B suggests this exact approach, making it the optimal choice for the initial version of the product.
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You are working for a company that is developing a new video streaming platform and you have been tasked with creating a recommendation system to suggest the next video for a user to watch. After being reviewed and approved by an AI Ethics team, you are ready to start the development process. Each video in your company’s catalog has useful metadata such as content type, release date, and country of origin, but there is no historical user event data available. In this scenario, what approach should you take to build the recommendation system for the first version of the product?
A
Launch the product without machine learning. Present videos to users alphabetically, and start collecting user event data so you can develop a recommender model in the future.
B
Launch the product without machine learning. Use simple heuristics based on content metadata to recommend similar videos to users, and start collecting user event data so you can develop a recommender model in the future.
C
Launch the product with machine learning. Use a publicly available dataset such as MovieLens to train a model using the Recommendations AI, and then apply this trained model to your data.
D
Launch the product with machine learning. Generate embeddings for each video by training an autoencoder on the content metadata using TensorFlow. Cluster content based on the similarity of these embeddings, and then recommend videos from the same cluster.