Media content is recommended based on suitability for a designated activity. A vector engine is trained using a plurality of lists, each of the lists containing metadata associated with a plurality of media objects. The vector engine includes a neural network trained with corpus data including (i) the plurality of lists (ii) a plurality of titles, each one of the titles associated with one of the lists, and (iii) the metadata associated with the plurality of media objects. Training the vector engine involves initializing, using the vector engine, a plurality of feature vectors representing each of the lists, each of the media objects, and each of a plurality of words in the titles of the lists. The training then further involves nudging, using the vector engine, the feature vectors based on a plurality of co-occurrences of the lists, the media objects, the words in the titles of the lists, or a combination thereof. A feature vector corresponding to an activity is identified among the feature vectors. At least one of the media objects, (ii) at least one of lists or (iii) a combination thereof suitable for the activity is selected based on cosine similarities between the feature vector corresponding to the activity and others of the feature vectors.