When building a content-based recommender system, what is crucial regarding the representation of items and users?

In a content-based recommender system, it is vital for both items and users to be expressed in the same embedding space. This alignment allows the system to draw meaningful comparisons and make recommendations based on the similarity between the user's preferences and the characteristics of the items. When users and items share the same representation space, the recommendation algorithm can effectively measure distances or similarities between them.

By modeling users and items within the same dimensionality, the system can leverage techniques such as cosine similarity or Euclidean distance to find the closest items that match a user's profile. This facilitates better understanding of user preferences and generates recommendations that are more relevant and accurate.

Options suggesting different dimensions or prioritizing one over the other would hinder the matching process, making it difficult for the model to evaluate and pair users with suitable items effectively. Prioritizing users or item representations would not cater to a balanced recommendation process where both need coherent contextual expressions for effective matching. Thus, expressing both users and items in the same embedding space is crucial for building a successful content-based recommendation system.