Given the task definition and input, reply with output. In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

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 Semantic Analysis approach for Recommendation 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.
SAR: A Semantic Analysis Approach for Recommendation