Table 2: Overview of collaborative filtering techniques.

CF categoriesRepresentative techniquesMain advantagesMain shortcomings

Memory-based CF eighbor-based CF (item-based/user-based CF algorithms with Pearson/vector cosine correlation) asy implementation *are dependent on human ratings
ew data can be added easily and incrementally *performance decrease when data are sparse
tem-based/user-based top- recommendations eed not consider the content of the items being recommended *cannot recommend for new users and items
*scale well with co-rated items *have limited scalability for large datasets

Model-based CF ayesian belief nets CF *better address the sparsity, scalability and other problems *expensive model-building
lustering CF
DP-based CF *improve prediction performance *have trade-off between prediction performance and scalability
atent semantic CF
parse factor analysis *give an intuitive rationale for recommendations *lose useful information for dimensionality reduction techniques
F using dimensionality reduction techniques, for example, SVD, PCA

Hybrid recommenders ontent-based CF recommender, for example, Fab *overcome limitations of CF and content-based or other recommenders *have increased complexity and expense for implementation
ontent-boosted CF *improve prediction performance *need external information that usually not available
ybrid CF combining memory-based and model-based CF algorithms, for example, Personality Diagnosis *overcome CF problems such as sparsity and gray sheep