  Recommendation system is a common demand in daily life and matrix completion
is a widely adopted technique for this task. However, most matrix completion
methods lack semantic interpretation and usually result in weak-semantic
recommendations. To this end, this paper proposes a $S$emantic $A$nalysis
approach for $R$ecommendation systems $(SAR)$, which applies a two-level
hierarchical generative process that assigns semantic properties and categories
for user and item. $SAR$ learns semantic representations of users/items merely
from user ratings on items, which offers a new path to recommendation by
semantic matching with the learned representations. Extensive experiments
demonstrate $SAR$ outperforms other state-of-the-art baselines substantially.
