Recommendation is scientific department which importance correlates with the number of options user have when he wants to choose a product in random domain. In the age of informatics, recommender systems are very helpful, because a common person is experiencing big information overload. Similarly they are very helpful for sellers, because they try to offer customers something they like and probability that they will actually like it is higher.
The most common recommendation technique is collaborative recommendation which rates the product based on ratings of similar users. But it cant generate quality suggestions if it operates in environment where there were made only a few interactions between the users and products. Its called problem of sparsity.
In this thesis I want to create hybrid recommendation system, which will combine collaborative recommender with the second most common type of recommender, content-based. Output from content-based recommender systems are products, which have common features with those, which were rated positively by the user. I will analyze which features are good to combine with standard collaborative recommender and then try to prove their usefulness by implementing and testing hybrid recommender system on some classic domain.