We use a binary attribute representation (BAR) model to describe a data set of Netflix viewers' ratings of movies. We classify the viewers with discrete bits rather than continuous parameters, which makes the representation compact and transparent. The attributes are easy to interpret, and we need far fewer attributes than similar methods do to achieve the same level of error. We also take advantage of the nonuniform distribution of ratings among the movies in the data set to train on a small selection of movies without compromising performance on the rest of the movies.

翻译：我们采用二元属性表示（BAR）模型来描述Netflix观影者对电影的评分数据集。该方法利用离散比特而非连续参数对观影者进行分类，从而使表示形式既紧凑又透明。这些属性易于解读，并且与同类方法相比，我们仅需远少于其数量的属性即可达到同等误差水平。我们还利用了数据集中各电影评分分布的非均匀性，仅通过少量电影样本进行训练，且不影响其余电影的预测性能。