Abstract: A system and method for matching students to tutors is disclosed. In particular, a user submits a query and in response a plurality of tutors are identified responsive to the search query. The identified tutors are then ranked based on relevance to the search query. The identified tutors are then presented to the user in an order based on the relevance score assigned to each tutor.

Abstract: A system and method for utilizing automatically generating related subject matter areas to create footer links for a subject matter page of a tutor matching website is disclosed and claimed. In particular, a document embedding algorithm such as word2vec, doc2vec or GloVe can be trained using on a particular subject using pertinent material such as, for example, textbooks, learned papers, and transcripts of lectures. Once trained, the document embedding algorithm can be used to generate a list of related subjects that can be used to automatically build footer links for a particular subject matter page. The related footer links can improve the positioning of the tutor matching website with common Internet search engines. The same document embedding algorithm can be used to identify previously answered questions that may assist a user posing a question to the tutor matching website.

Abstract: A system and method for matching students to tutors is disclosed. In particular, a user submits a query and in response a plurality of tutors are identified responsive to the search query. The identified tutors are then ranked based on relevance to the search query. The identified tutors are then presented to the user in an order based on the relevance score assigned to each tutor.

Abstract: A system and method for utilizing automatically generating related subject matter areas to create footer links for a subject matter page of a tutor matching website is disclosed and claimed. In particular, a document embedding algorithm such as word2vec, doc2vec or GloVe can be trained using on a particular subject using pertinent material such as, for example, textbooks, learned papers, and transcripts of lectures. Once trained, the document embedding algorithm can be used to generate a list of related subjects that can be used to automatically build footer links for a particular subject matter page. The related footer links can improve the positioning of the tutor matching website with common Internet search engines. The same document embedding algorithm can be used to identify previously answered questions that may assist a user posing a question to the tutor matching website.