Association rules have been used for many years in merchandising, both to analyze patterns of preference across products, and to
recommend products to consumers based on other products they have selected. An association rule expresses the relationship that
one product is often purchased along with other products. The number of possible association rules grows exponentially with the
number of products in a rule, but constraints on confidence and support, combined with algorithms that build association rules with
itemsets of n items from rules with n-1 item itemsets, reduce the effective search space. Association rules can form a very compact
representation of preference data that may improve efficiency of storage as well as performance. They are more commonly used for
larger populations rather than for individual consumers, and they, like other learning methods that first build and then apply models,
are less suitable for applications where knowledge of preferences changes rapidly. Association rules have been particularly
successfully in broad applications such as shelf layout in retail stores. By contrast, recommender systems based on nearest neighbor
techniques are easier to implement for personal recommendation in a domain where consumer opinions are frequently added, such as
on-line retail.
Association rules have been used for many years in merchandising, both to analyze patterns of preference across products, and to
recommend products to consumers based on other products they have selected. An association rule expresses the relationship that
one product is often purchased along with other products. The number of possible association rules grows exponentially with the
number of products in a rule, but constraints on confidence and support, combined with algorithms that build association rules with
itemsets of n items from rules with n-1 item itemsets, reduce the effective search space. Association rules can form a very compact
representation of preference data that may improve efficiency of storage as well as performance. They are more commonly used for
larger populations rather than for individual consumers, and they, like other learning methods that first build and then apply models,
are less suitable for applications where knowledge of preferences changes rapidly. Association rules have been particularly
successfully in broad applications such as shelf layout in retail stores. By contrast, recommender systems based on nearest neighbor
techniques are easier to implement for personal recommendation in a domain where consumer opinions are frequently added, such as
on-line retail.
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