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2. Making Recommendations > User-Based or Item-Based Filtering?

User-Based or Item-Based Filtering?

Item-based filtering is significantly faster than user-based when getting a list of recommendations for a large dataset, but it does have the additional overhead of maintaining the item similarity table. Also, there is a difference in accuracy that depends on how “sparse” the dataset is. In the movie example, since every critic has rated nearly every movie, the dataset is dense (not sparse). On the other hand, it would be unlikely to find two people with the same set of del.icio.us bookmarks—most bookmarks are saved by a small group of people, leading to a sparse dataset. Item-based filtering usually outperforms user-based filtering in sparse datasets, and the two perform about equally ....


  

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