  Collaborative Filtering (CF), the most common approach to build Recommender
Systems, became pervasive in our daily lives as consumers of products and
services. However, challenges limit the effectiveness of Collaborative
Filtering approaches when dealing with recommendation data, mainly due to the
diversity and locality of user preferences, structural sparsity of user-item
ratings, subjectivity of rating scales, and increasingly high item
dimensionality and user bases. To answer some of these challenges, some authors
proposed successful approaches combining CF with Biclustering techniques.
  This work assesses the effectiveness of Biclustering approaches for CF,
comparing the impact of algorithmic choices, and identifies principles for
superior Biclustering-based CF. As a result, we propose USBFC, a
Biclustering-based CF approach that creates user-specific models from strongly
coherent and statistically significant rating patterns, corresponding to
subspaces of shared preferences across users. Evaluation on real-world data
reveals that USBCF achieves competitive predictive accuracy against
state-of-the-art CF methods. Moreover, USBFC successfully suppresses the main
shortcomings of the previously proposed state-of-the-art biclustering-based CF
by increasing coverage, and coclustering-based CF by strengthening subspace
homogeneity.
