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14-Dec-2019 05:47

from different services can be recommended based on news browsing.As previously detailed, Pandora Radio is a popular example of a content-based recommender system that plays music with similar characteristics to that of a song provided by the user as an initial seed.When building a model from a user's behavior, a distinction is often made between explicit and implicit forms of data collection.Examples of explicit data collection include the following: The recommender system compares the collected data to similar and dissimilar data collected from others and calculates a list of recommended items for the user.These approaches are often combined (see Hybrid Recommender Systems).The differences between collaborative and content-based filtering can be demonstrated by comparing two popular music recommender systems – and Pandora Radio.This is an example of the cold start problem, and is common in collaborative filtering systems.

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Simple approaches use the average values of the rated item vector while other sophisticated methods use machine learning techniques such as Bayesian Classifiers, cluster analysis, decision trees, and artificial neural networks in order to estimate the probability that the user is going to like the item.Many algorithms have been used in measuring user similarity or item similarity in recommender systems.For example, the k-nearest neighbor (k-NN) approach Collaborative filtering is based on the assumption that people who agreed in the past will agree in the future, and that they will like similar kinds of items as they liked in the past.When the system is limited to recommending content of the same type as the user is already using, the value from the recommendation system is significantly less than when other content types from other services can be recommended.

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For example, recommending news articles based on browsing of news is useful, but would be much more useful when music, videos, products, discussions etc.Public health professionals have been studying recommender systems to personalize health education and preventative strategies.