推荐系统的分类

推荐系统的分类(Taxonomy of Recommender Systems)

推荐算法

###1. Non-Personalized summary Statistics

  • External Community Data
    –Best-seller;Most popular;Trending Hot
  • summary of Community Ratings
    –Best-liked 例如: 音乐排行榜

###2. Content-Based Filtering (基于内容的过滤)
–information filtering 信息过滤
–knowledge-based 基于知识

User Rating X item Attributes => Model
Model applied to new items via attributes
alternative : knowledge-based
-Item attributes form model of item space
–users navigate/browse that space
例如: 

 - Personalized news feeds  
- Artist or Genre music feeds

###3. Collaborative Filtering (协同过滤)

  • User-User
    Select neighborhood of similar-taste people
    variant: select people you know/trust
    Use their opinions

  • Item-Item
    Pre-compute similarity among items via ratings
    use own ratings to triangulate for recommendations

  • Dimensionality Reduction (奇异值分解 SVD)
    create lower dimensional matrix through mathmetic methods
    intuition: taste yields a lower-dimensionality matrix
    Compress and use a taste representation

Personalized Collaborative Filtering

  • Use opinions of others to predict/recommend
  • User-model – set of ratings
  • item-model – set of ratings
  • common core: sparse matrix of ratings
    –Fill in missing values(predict)
    –Select promising cells(recommend)
  • Several different techniques

###4. Others
–Critique/Interview Based Recommendations
–Hybrid Techniques

From The Abstrct to the Specific(从抽象到具体)

Basic Model

--User  
--Ratings  
--Item

注意评估

为了更好的理解每种算法的好处,我们会花大量的时间来评估
1.推荐的准确度
2 推荐的有用性 
3.计算性能

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