what are rater vectors
Rater vectors, also known as rating vectors, are mathematical constructs used in the field of recommendation systems. A recommendation system is a system that provides personalized recommendations to users based on their preferences or past behavior.
In the context of recommendation systems, a rater vector represents the preferences or ratings given by a user for various items. It is a vector that captures the user's opinion or preference for each item in the system. Each element of the vector corresponds to an item, and the value of the element indicates the user's rating or preference for that item.
To understand how rater vectors are used, let's consider an example. Let's say we have a movie recommendation system, and we want to recommend movies to a user based on their ratings of similar movies. We can represent the user's ratings as a rater vector, where each element represents a movie and the value of the element represents the rating given by the user for that movie, such as a scale from 1 to 5.
To get the rater vector for a user, we often rely on explicit feedback where users explicitly rate items. However, in some cases, implicit feedback can also be used, where the user's behavior (such as clicks, views, or purchase history) is analyzed to infer their preferences and construct the rater vector.
Once we have the rater vectors for multiple users, we can compare them to find similar users or similar items. This comparison is usually done using techniques like cosine similarity or Pearson correlation. By identifying similar users, we can recommend items that were highly rated by those users but not yet seen by the target user.
Overall, rater vectors are key components of recommendation systems, allowing us to capture user preferences and make personalized recommendations based on those preferences.