A Game with a Purpose for Understanding Latent Factors

By leveraging the crowd this Game with a Purpose allows to elicit descriptions for the dimensions of a Matrix Factorization model, this way revealing the otherwise hidden semantics of the underlying latent factors. The game follows the output-agreement method and shows representative items for each latent factor to randomly matched pairs of users. For each factor consecutively, the two players then type in terms that they think describe best the commonalities of the respective items until a match is found. By analyzing the terms collected, meaningful descriptions for each factor can be derived as a by-product of gameplay. In this manner, the online game contributes to a better understanding of latent factors in Recommender Systems.

Related research topics


Johannes Kunkel


Further contributors

Benedikt Loepp


Ali Sercan Basyurt



Understanding Latent Factors Using a GWAP

Ein Online-Spiel zur Benennung latenter Faktoren in Empfehlungssystemen