Techniques for using a machine-learned model to personalize content item density. In one technique, an entity that is associated with a content request is identified. Multiple sets of content items are identified that includes content items of different types. A first position of a first slot is determined in a content item feed that comprises multiple slots. A second position of a previous content item is determined, in the content item feed, that is of a first type. A difference between the first position and the second position is determined. Based on the difference, a gap sensitivity value that is associated with the entity and is different than the difference is determined. Based on the gap sensitivity value, a content item from the multiple sets of content items is selected and inserted into the first slot. The content item feed is transmitted to a computing device to be presented thereon.