Graph-based recommendations: from data representation to feature extraction and application

Abstract

Modeling users for the purpose of identifying their preferences and then personalizing services on the basis of these models is a complex task, primarily due to the need to take into consideration various explicit and implicit signals, missing or uncertain information, contextual aspects, and more. In this study, a novel generic approach for uncovering latent preference patterns from user data is proposed and evaluated. The approach relies on representing the data using graphs, and then systematically extracting graph-based features and using them to enrich the original user models. The extracted features encapsulate complex relationships between users, items, and metadata. The enhanced user models can then serve as an input to any recommendation algorithm. The proposed approach is domain-independent (demonstrated on data from movies, music, and business recommender systems), and is evaluated using several state-of-the-art machine learning methods, on different recommendation tasks, and using different evaluation metrics. The results show a unanimous improvement in the recommendation accuracy across tasks and domains. In addition, the evaluation provides a deeper analysis regarding the performance of the approach in special scenarios, including high sparsity and variability of ratings.

Addressing Item Cold Start Problem in Collaborative Filtering-Based Recommender Systems Using Auxiliary Information

“…Graphs and network are increasingly applied for user modeling. The main advantage of these models is that they allow combining entities and their mutual links in simple structures, without loss of information [41]. At the same time, graph learning methods, such as random walk methods, graph neural networks methods and graph embedding methods, capable of learning complex relations between entities and extracting knowledge from graphs, were developed [46].…”

“…Graph-based methods provide versatile structures for representing the relationships among users and items [2,29,41]. In recent years, graph learning (GL) approaches have been developed for graph-based user modeling.…”