Developer Advocate Wei Wei overviews common approaches to tackle 3 kinds of cold start problems. When a recommendation system does not have enough information on the users or the candidate items, it leads to ineffective recommendations and what's known as a cold start. Learn a few strategies to mitigate user, item, and system cold starts.
Resources:
Sequential recommendations with TensorFlow Recommenders → [ Ссылка ]
Content-based filtering → [ Ссылка ]
Recommendation with TF Agents Bandits Library → [ Ссылка ]
Applying the hashing trick to an integer categorical feature (Keras) → [ Ссылка ]
Subscribe to TensorFlow → [ Ссылка ]
