Much efforts have lately been concentrated on increasing the precision of recommendations following the Netflix Prize competition. Recently, many researchers and industries have noted that other factors like adequate presentation of the results can add more utility to a recommender system than slight improvement in the precision. In this paper, we suggest a methodology for user-friendly representation of recommendations to the end users. Our scheme unifies the two objectives of prediction and visualization in the core of a unique approach. Users and items are first embedded into a high dimensional latent feature space according to a predictor function, particularly designated to meet visualization requirements. The data is then projected into a $2$-dimensional space by Curvilinear Component Analysis (CCA). CCA draws personalized Item Maps (PIMs) representing a small subset of items to the active user. The intra-item semantic correlations are preserved in PIMs which is inherited from the clustering property of the high-dimensional embedding space. Our prediction function and the projection method are both non-linear to increase the clarity of the maps and to limit the effect of projection error. The algorithms are tested on three versions of the MovieLens dataset and the Netflix dataset to show they combine good accuracy with satisfactory visual properties.
