Abstract: A computer-implemented suggestion engine suggests documents to a requesting user based on significant words in the documents and document metadata. Embodiments determine dictionaries for each document in a content repository as well as one or more documents comprising a basis data set. Embodiments then query the content repository with significant n-grams from the basis data set's dictionary. Embodiments return one or more documents with matching n-grams as a result set, and then filter the result set before providing one or more documents from the result set to the user. Embodiments can also suggest documents based on inferred document metadata. For example, embodiments can infer geographic location information about a document based on metadata associated with the document's neighbors (e.g., other documents saved in the same user folder). Embodiments can use the inferred information to suggest geographically relevant documents to the user.