Uploaded December 2022 | Updated September 2026, 1 week ago
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 → https://goo.gle/3uDEaao
Content-based filtering → bit.ly/3UJwfmC
Recommendation with TF Agents Bandits Library → https://goo.gle/3HshuRY
Applying the hashing trick to an integer categorical feature (Keras) → https://goo.gle/3UFD0pf
Subscribe to TensorFlow → https://goo.gle/TensorFlow
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 → https://goo.gle/3uDEaao
Content-based filtering → bit.ly/3UJwfmC
Recommendation with TF Agents Bandits Library → https://goo.gle/3HshuRY
Applying the hashing trick to an integer categorical feature (Keras) → https://goo.gle/3UFD0pf
Subscribe to TensorFlow → https://goo.gle/TensorFlow
