Algorithms that discover regular patterns in large collections of records and use them to suggest what a user may want. The Apriori algorithm finds sets of items that often occur together in transactions, sequential pattern mining looks for patterns that occur in a particular order, and recommender systems use such patterns about people and items to predict which products, films or pages a person will like.
Pattern Mining and Recommendation Algorithms
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Collaborative filtering (Wikipedia)
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Collaborative filtering (CF) is, besides content-based filtering, one of two major techniques used by recommender systems.
Wikipedia: Apriori algorithm
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Apriori is an algorithm for frequent item set mining and association rule learning over relational databases.
Wikipedia: Sequential pattern mining
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Sequential pattern mining is a topic of data mining concerned with finding statistically relevant patterns between data examples where the values are delivered in a sequence.
Recommender Systems (Wikipedia)
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A recommender system, also called a recommendation engine or content discovery platform is a type of information filtering system that aims to suggest items most relevant for some input or to a particular user.
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