An AI model's rating system must clearly separate different rating contexts. A rejected item is not the same as an unrated item. Different views of an item can have different maximum ratings.
By Pawan Nayar · from the LLOS AI Learning record
An AI model's rating system must clearly separate different rating contexts.
A rejected item is not the same as an unrated item. Different views of an item can have different maximum ratings.
A grid view showing many items together has a maximum rating of two stars. A detailed view of one item allows a maximum rating of five stars for each part of that item. A rejected item gets zero stars. An item with no rating key is unrated. These two states are different.
A developer creates an AI model that suggests shapes. In a grid view, a user can give a shape up to two stars. When the user clicks on a shape, a detailed view opens. The user can then rate the shape's design and its material separately, each up to five stars. If the user rejects a shape, it automatically gets zero stars. If the user does not rate a shape, it remains unrated. The system does not confuse a rejected shape with an unrated shape.
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An AI model's rating system must clearly separate different rating contexts. A rejected item is not the same as an unrated item. Different views of an item can have different maximum ratings.