1. A method comprising: generating a plurality of model-generated scores; wherein each model-generated score of the plurality of model-generated scores corresponds to a candidate image from a plurality of candidate images for a particular video item; wherein generating the plurality of model-generated scores includes, for each candidate image of the plurality of candidate images, using a set of input parameter values with a trained machine learning engine to produce the model-generated score that corresponds to the candidate image, wherein the set of input parameter values include at least one input parameter value for an activity feature that reflects one or more actions that one or more users have performed, during playback of the particular video item, relative to a frame that corresponds to the particular candidate image; establishing a ranking of the candidate images, from the plurality of candidate images, for the particular video item based, at least in part, on the model-generated scores that correspond to the candidate images; selecting a candidate image, from the plurality of candidate images, as a representative image for the particular video item based, at least in part, on the ranking; wherein the method is performed by one or more computing devices.
1. A method comprising: generating a plurality of model-generated scores; wherein each model-generated score of the plurality of model-generated scores corresponds to a candidate image from a plurality of candidate images for a particular video item; wherein generating the plurality of model-generated scores includes, for each candidate image of the plurality of candidate images, using a set of input parameter values with a trained machine learning engine to produce the model-generated score that corresponds to the candidate image, wherein the set of input parameter values include at least one input parameter value for an activity feature that reflects one or more actions that one or more users have performed, during playback of the particular video item, relative to a frame that corresponds to the particular candidate image; establishing a ranking of the candidate images, from the plurality of candidate images, for the particular video item based, at least in part, on the model-generated scores that correspond to the candidate images; selecting a candidate image, from the plurality of candidate images, as a representative image for the particular video item based, at least in part, on the ranking; wherein the method is performed by one or more computing devices. 10. The method of claim 1 further comprising employing an explore/exploit technique to test actual user responses to different representative images of the particular video item.