A recommender system is a technology that is deployed in the environment where items (products, movies, events, articles) are to be recommended to users (customers, visitors, app users, readers) or the opposite. Typically, there are many items and many users present in the environment making the problem hard and expensive to solve. Imagine a shop. Good merchant knows personal preferences of customers. Her/His high quality recommendations make customers satisfied and increase profits. In case of online marketing and shopping, personal recommendations can be generated by an artificial merchant: the recommender system.
Content based recommender systems
Such systems are recommending items similar to those a given user has liked in the past, regardless of the preferences of other users. Basically, there are two different types of feedback.
Explicit feedback is intentionally provided by users in form of clicking the “like”/”dislike” buttons, rating an item by number of stars, etc. In many cases, it is hard to obtain explicit feedback data, simply because the users are not willing to provide it. Instead of clicking “dislike” for an item which the user does not consider interesting, he/she will rather leave the web page or switch to another TV channel.
Implicit feedback data, such as “user viewed an item”, “user finished reading the article” or “user ordered a product”, however, are often much easier to collect and can also help us to compute good recommendations.