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Advantages
The model doesn't need any data about other users, since
the recommendations are specific to this user. This makes
it easier to scale to a large number of users.
The model can capture the specific interests of a user,
and can recommend niche items that very few other users
are interested in.
Disadvantages
Since the feature representation of the items are hand-engineered
to some extent, this technique requires a lot of domain knowledge. Therefore,
the model can only be as good as the hand-engineered features.
The model can only make recommendations based on existing interests of
the user. In other words, the model has limited ability to expand on the
users' existing interests.