The cold start problem
The cold start problem in recommendation systems refers to the challenge of making accurate recommendations for new users or new items that have little to no interaction data. In movie recommendation systems, this occurs when a new user joins the platform and has no viewing history or when a new movie is released and has no user ratings or interactions.
The cold start problem is particularly challenging in movie recommendation systems, especially for new users or newly released movies.
Graph embedding transfer
One solution to the cold start problem is graph embedding transfer, a technique that’s used to initialize representations for new nodes (movies or users) in a recommendation graph when there’s no interaction data available. Here’s a general description:
- For new items (for example, movies):
- Identify similar existing items based on metadata or content features.
- Use the embeddings of these similar items to initialize the...
