Summary on Shusen Wang's tutorial: Look-alike Prediction Recommendation

 Starting with seed users, from whom we know the users' background: age group, education level, income, etc. Find users who didn't provide much information by similarity, including User Collaborative Filtering.

Focus on the users who interacted with a new item - treat them as seed users. Take an average on the seed user to get a vector. Use this vector to spread to look-alike users (by looking up in a vector db). Note that this feature vector needs to be updated when more users are interacting with this item.

Reference: https://www.youtube.com/watch?v=pjmRo8Uzzqg