GameSage is a tool that takes free-text input describing an idea for a videogame and lists the existing games that are most related to that idea. This tool utilizes the notion in LSA of folding in, whereby a new document that was not used during model training is fitted with a representation in the semantic space derived by the model. By treating the user’s input text (which specifies her game idea) as a corpus document (on par with the videogame Wikipedia articles we used to train our LSA model) and folding it in, we are able to derive an LSA vector for the idea. From here, we determine which existing games (from among GameNet’s 11,829 games) are most related to the game idea by using cosine similarity, just as we did in constructing GameNet.

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