The usage of recommender systems and their impact on everyday life has gained a lot of importance in recent years. The primary objective is to guide users to the discovery of new products and services by providing suggestions based on already known user interests and ratings. This is an essential feature in the digital world, since users tend to easily get overwhelmed by choice if given a large number of items to choose from. There is a big variety of areas in which recommender systems are used, like product recommendations for online shopping stores or artist-based song recommenders for music streaming platforms. As the datasets for the respective recommendation tool can differ in size and structure, it is crucial to find the best suiting recommendation technique for rating predictions out of a big collection of different approaches and methods. A complication that can occur in recommender systems is the cold-start problem, which refers to an issue in which it is challenging for the system to infer interactions between users and items due to insufficient information. This thesis introduces recommender systems in general and how they can be approached with different techniques as well as possible solutions for the cold start problem. The presented dataset describes a case of the cold-start problem, which needs to be resolved in recommender systems. For this purpose, this thesis describes an analysis of existing recommendation methods and discusses their suitability for solving the cold-start problem for the arXiv dataset. The results achieved in this thesis can provide essential insights into how to deal with the cold-start problem in datasets that do not contain any user records.     «

The usage of recommender systems and their impact on everyday life has gained a lot of importance in recent years. The primary objective is to guide users to the discovery of new products and services by providing suggestions based on already known user interests and ratings. This is an essential feature in the digital world, since users tend to easily get overwhelmed by choice if given a large number of items to choose from. There is a big variety of areas in which recommender systems are used,...     »