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Seminar Topic: Scientific Recommender Systems

10 Nov

In my seminar topic I have to deal with the problem of how to recommend scientific literature that might be intersting to a specific user.
You probably all already encountered recommender systems in other domains and contexts (e.g. on amazon: “Customers Who Bought This Item Also Bought”).

Basically, recommender systems can be classified into three categories:

  • content-based (items are recommended that are similar to items the user preferred before)
  • collaborative (items are recommended that other users with similar tastes/preferences liked)
  • hybrid approaches (combination of content-based and collaborative methods)

In my presentation and article I will take a closer look at these categories and corresponding recommender systems, their advantages and disadvantages and I will introduce some algorithms which are used to calculate which items will be recommended.

 

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