Recommendation systems make personalized recommendations for users based on their input. They learn from user feedback and are commonly used on retail and review sites. They improve with more data and can be personalized for marketing.
Recommendation systems are systems that make recommendations for users based on data entered by users into the system. The more data a user has provided, the more accurate such systems can be. Furthermore, the data submitted by individual users helps to improve the system in general, generating information that can be used to make recommendations for other users. Recommender systems are commonly seen on sites such as film and television review sites and those with large inventories of retail items that would be functionally impossible to browse by looking at every item.
These systems can interact with users in different ways. One is as a service for users to search for more things they might be interested in, such as further reading, television shows, or video games. In these systems, the user generates a list of likes and dislikes, and the system tries to predict how the user will vote on things they haven’t voted on yet. If it thinks something has a high rating, it suggests it to the user.
Well-designed recommender systems learn from their mistakes. A system might recommend The Sound of Music because a user likes Willy Wonka and the Chocolate Factory. The user can select options such as “I like it” or “I don’t like it”. If the user does not like The Sound of Music, the system may take notice and further refine the algorithm used to generate recommendations. The more data that is accumulated, the more useful the recommendations will be.
Retail sites use recommender systems to get people to make impulse buys. The system takes note of the items purchased and recommends related and useful items. For example, someone who is buying a camera might be asked if they would like to purchase a charger, camera case, filters, and additional lenses. Someone buying a book on feminist theory might be told that other buyers of that title also enjoy another related title. These types of recommender systems allow for personalized marketing that is highly likely to appeal to users.
These systems are based on collaborative data filtering, where the data of a large number of users is organized in a meaningful way. This allows the site to make connections that might not otherwise be apparent, improving the quality of recommendations. Users who do not want to participate can usually change the options in their user settings, but it will reduce the quality of the recommendations they receive because the system cannot learn from individual preferences, only from the collective opinion of other users.
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