November 2017
Beginner to intermediate
366 pages
7h 59m
English
Content preview from R Data Analysis Projects
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Item-based models
This is a model-based approach. From a given ratings matrix, this method explores the relationship between the items. Based on the ratings, different users provide different items, and an item-to-item similarity matrix is derived. Once again, as in the user-based model, Pearson coefficient or cosine distance is used as a similarity metric. For each item, we store the top K similar items, rather than storing all the items for efficiency purposes. A weighted sum idea is used to finally make a recommendation for a user. Refer to the paper from Amazon for more about item-based filtering: https://dl.acm.org/citation.cfm?id=642471
The following code shows how to perform item-based recommendations:
> plan <- evaluationScheme(data, ...
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