A Survey of Collaborative Filtering Techniques

Table 2

Overview of collaborative filtering techniques.

CF categories	Representative techniques	Main advantages	Main shortcomings
Memory-based CF	^∗Neighbor-based CF (item-based/user-based CF algorithms with Pearson/vector cosine correlation)	^∗easy implementation	*are dependent on human ratings
		^∗new data can be added easily and incrementally	*performance decrease when data are sparse
	^∗Item-based/user-based top-𝑁 recommendations	^∗need 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	^∗Bayesian belief nets CF	*better address the sparsity, scalability and other problems	*expensive model-building
	^∗clustering CF
	^∗MDP-based CF	*improve prediction performance	*have trade-off between prediction performance and scalability
	^∗latent semantic CF
	^∗sparse factor analysis	*give an intuitive rationale for recommendations	*lose useful information for dimensionality reduction techniques
	^∗CF using dimensionality reduction techniques, for example, SVD, PCA
Hybrid recommenders	^∗content-based CF recommender, for example, Fab	*overcome limitations of CF and content-based or other recommenders	*have increased complexity and expense for implementation
	^∗content-boosted CF	*improve prediction performance	*need external information that usually not available
	^∗hybrid CF combining memory-based and model-based CF algorithms, for example, Personality Diagnosis	*overcome CF problems such as sparsity and gray sheep