Augmenting Netflix Search with In-Session Adapted Recommendations
arXiv:2206.02254v110 citationsh-index: 7
Originality Synthesis-oriented
AI Analysis
This addresses the need for more responsive recommendation systems for streaming service users, but appears incremental as it builds on existing adaptive methods.
The paper tackles the problem of adapting Netflix Search recommendations to users' in-session intent by leveraging their current interactions, resulting in an end-to-end system designed for production scale.
We motivate the need for recommendation systems that can cater to the members in-the-moment intent by leveraging their interactions from the current session. We provide an overview of an end-to-end in-session adaptive recommendations system in the context of Netflix Search. We discuss the challenges and potential solutions when developing such a system at production scale.