推荐系统广泛地应用在网络平台中，推荐模型需要预测用户的喜好，帮助用户找到适合的电影、书籍、音乐等商品.通过对用户评分和评论信息的分析，可以发现用户关注的商品特征，并根据商品的特征，推测用户对该商品的喜好程度.本文提出将评论中隐含的语义内容与评分相结合，设计并实现了一种新颖的商品推荐模型.首先利用主题模型挖掘评论文本中隐含的主题分布，用主题分布刻画用户偏好和商品画像，在逻辑回归模型上训练主题与打分的关系，最终评分可以被视为是对用户偏好和商品画像的相似程度的量化表示.最后，本文在真实数据上进行了大量对比实验，结果证明该模型比对比系统性能优越且稳定.

Recommender systems are widely deployed in Web applications that need to predict the preferences of users to items. They are popular in helping users find movies, books, music, and products in general. In this work, we design a method for item recommendation based on a novel model that captures correlations between hidden aspects in reviews and numeric ratings. It is motivated by the observation that a user’s preference against an item is affected by different aspects discussed in reviews. Our method first explores topic modeling to discover hidden aspects from review text. Profiles are then created for users and items separately based on aspects discovered in their reviews. Finally, we utilize logistic regression to model the useritem relationship and the rating is modeled as the similarity between user and item profiles. Experiments over real world reviews demonstrate the advantage of our proposal over state of the art solution.