Explain Data Mining in Recommender Systems.

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 Data Mining in  Recommender Systems

  • Today's consumers are faced with millions of goods and services when shopping online. Recommender systems help consumers by making product recommendations that are likely to be of interest to the user such as books, CDs, movies, restaurants, online news articles, and other services. Recommender systems may use either a content-based approach, a collaborative approach, or a hybrid approach that combines both content-based and collaborative methods.
  • The content-based approach recommends items that are similar to items the user preferred or queried in the past. It relies on product features and textual item descriptions. The collaborative approach (or collaborative filtering approach) may consider a user's social environment. It recommends items based on the opinions of other customers who have similar tastes or preferences as the user. Recommender systems use a broad range of techniques from information retrieval, statistics, machine learning, and data mining to search for similarities among items and customer preferences.
  • An advantage of recommender systems is that they provide personalization for customers of e-commerce, promoting one-to-one marketing. Amazon, a pioneer in the use of collaborative recommender systems, offers "a personalized store for every customer as part of their marketing strategy. Personalization can benefit both consumers and the company involved. By having more accurate models of their customers, companies gain a better understanding of customer needs. Serving these needs can result in greater success regarding cross-selling of related products, upselling, product affinities, one-to-one promotions, larger baskets, and customer retention.


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