BookLikeThat is a Python-based book recommendation engine which leverages NLTK's various computational tools to recommend, based off one text that the user already likes, whether another text may be enjoyable to read or not. Currently, texts already availble in NLTK's Gutenberg corpus are supported. TF-IDF scores assembled as arrays for each respective text are compared via cosine similarity, forming the backbone of the decision engine.