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.