Recommender System 初步认识

毕设里面要用到个性化的信息推荐，因此接触到Recommender system。了解一个事物，总是从wikipedia开始。

Recommender systems is a subclass of information filtering system that seek to predict the 'rating' or 'preference' that a user would give to an item (such as music, books, or movies) or social element (e.g. people or groups) they had not yet considered, using a model built from the characteristics of an item (content-based approaches) or the user's social environment (collaborative filtering approaches).

大意为：

推荐系统是信息过滤系统的一个子类，旨在在用户接触到某些东西之前，就预测出用户会给它们的评分和喜恶。对于评价一个物品的话，可以使用基于内容的方式，对于评价用户的社会环境，则可以使用协同过滤的方式。

Collaborative filtering approaches to build a model from a user's past behavior (items previously purchased or selected and/or numerical ratings given to those items) as well as similar decisions made by other users, then use that model to predict items (or ratings for items) that the user may have an interest in.

协同过滤的方式一般是根据用户之前对某项东西的评分来判断他与其他某群用户的兴趣相似程度，借由群体之间的兴趣相似程度来判断用户A是否也喜欢用户B买的X产品。（用户A和用户B兴趣相似，且用户B购买过X产品）。也就是说协同过滤是按照用户与物品之间的关系来做判断和计算的模型。

协同过滤可以参考这篇文章 Prem Melville and Vikas Sindhwani, Recommender Systems, Encyclopedia of Machine Learning, 2010.

Content-based filtering approaches utilize a series of discrete characteristics of an item in order to recommend additional items with similar properties.

基于内容的过滤可以参考这篇文章 R. J. Mooney and L. Roy (1999). "Content-based book recommendation using learning for text categorization". In Workshop Recom. Sys.: Algo. and Evaluation.

基于内容的过滤则是按照物品的属性来进行预测，比如说用户喜欢具有a属性的东西，而某商品X则刚好具有a属性，因此判断用户也许会喜欢物品X。它是基于物品的属性来做判断和计算模型的。跟协同过滤不同。

These approaches are often combined (see Hybrid Recommender Systems).

以上两种方法常常被结合起来使用，称为混合型的推荐系统。