Zastosowanie graf\'ow i sieci w systemach rekomendacji

The chapter aims to explore the application of graph theory and networks in the recommendation domain, encompassing the mathematical models that form the foundation for the algorithms and recommendation systems developed based on them. The initial section of the chapter provides a concise overview of the recommendation field, with a particular focus on the types of recommendation solutions and the mathematical description of the problem. Subsequently, the chapter delves into the models and techniques for utilizing graphs and networks, along with illustrative examples of algorithms constructed on their basis.Comment: in Polish language. Przedsi\k{e}biorstwo w nowej rzeczywisto\'sci gospodarczej. Relacje zmiany strategie; 202

Recommending on graphs: a comprehensive review from a data perspective

Recent advances in graph-based learning approaches have demonstrated their effectiveness in modelling users' preferences and items' characteristics for Recommender Systems (RSS). Most of the data in RSS can be organized into graphs where various objects (e.g., users, items, and attributes) are explicitly or implicitly connected and influence each other via various relations. Such a graph-based organization brings benefits to exploiting potential properties in graph learning (e.g., random walk and network embedding) techniques to enrich the representations of the user and item nodes, which is an essential factor for successful recommendations. In this paper, we provide a comprehensive survey of Graph Learning-based Recommender Systems (GLRSs). Specifically, we start from a data-driven perspective to systematically categorize various graphs in GLRSs and analyze their characteristics. Then, we discuss the state-of-the-art frameworks with a focus on the graph learning module and how they address practical recommendation challenges such as scalability, fairness, diversity, explainability and so on. Finally, we share some potential research directions in this rapidly growing area.Comment: Accepted by UMUA