Correct. When using a latent factor method, providing an explanation for for the prediction has been essentially intractable.
A common post-processing step for many teams (particularly BellKor/KorBell) is a KNN. The KNN's are used to provide a sort of confidence metric and if the anyone so decided, using the KNN derived network for explanation is pretty simple and not really cheating.
Edit: Yehuda might have made some breakthrough - He'll be presenting a paper at KDD "Factorization Meets the Neighborhood: a Multifaceted Collaborative Filtering Model", the paper will probably be released after the conference.
A common post-processing step for many teams (particularly BellKor/KorBell) is a KNN. The KNN's are used to provide a sort of confidence metric and if the anyone so decided, using the KNN derived network for explanation is pretty simple and not really cheating.
Edit: Yehuda might have made some breakthrough - He'll be presenting a paper at KDD "Factorization Meets the Neighborhood: a Multifaceted Collaborative Filtering Model", the paper will probably be released after the conference.