arXiv:2405.08465v1 Announce Type: cross
Abstract: Traditional recommendation proposals, including content-based and collaborative filtering, usually focus on similarity between items or users. Existing approaches lack ways of introducing unexpectedness into recommendations, prioritizing globally popular items over exposing users to unforeseen items. This investigation aims to design and evaluate a novel layer on top of recommender systems suited to incorporate relational information and suggest items with a user-defined degree of surprise. We propose a Knowledge Graph (KG) based recommender system by encoding user interactions on item catalogs. Our study explores whether network-level metrics on KGs can influence the degree of surprise in recommendations. We hypothesize that surprisingness correlates with certain network metrics, treating user profiles as subgraphs within a larger catalog KG. The achieved solution reranks recommendations based on their impact on structural graph metrics. Our research contributes to optimizing recommendations to reflect the metrics. We experimentally evaluate our approach on two datasets of LastFM listening histories and synthetic Netflix viewing profiles. We find that reranking items based on complex network metrics leads to a more unexpected and surprising composition of recommendation lists.
Designing a Novel Layer on Top of Recommender Systems
Traditional recommendation systems have typically focused on similarity between items or users, resulting in recommendations that are often predictable and lack surprise. However, introducing unexpectedness into recommendations can be crucial in providing users with fresh and novel experiences. In this article, we explore a novel layer that can be added on top of existing recommender systems to incorporate relational information and suggest items with a user-defined degree of surprise.
The Multi-disciplinary Nature of the Concepts
The concepts discussed in this investigation draw upon several disciplines, combining concepts from recommender systems, knowledge graphs, and complex network analysis. By incorporating knowledge graphs, which represent relationships between various entities, we can leverage the structural properties of the graph to influence the surprise factor in recommendations. Additionally, complex network metrics are used to analyze the impact of recommendations on the overall structure of the knowledge graph.
Relationship to Multimedia Information Systems
Recommendation systems play a crucial role in multimedia information systems by helping users discover relevant and engaging multimedia content. By incorporating a surprise factor into recommendations, the proposed approach can enhance the user experience by suggesting items that users may not have encountered otherwise. This can lead to a more diverse and engaging multimedia consumption, catering to the specific preferences and interests of individual users.
Related to Animations, Artificial Reality, Augmented Reality, and Virtual Realities
In the realm of animations, artificial reality, augmented reality, and virtual realities, recommendations play a vital role in guiding users towards immersive and enjoyable experiences. By incorporating surprise into these recommendations, users can be exposed to unexpected and exciting content that expands their horizons and enhances their engagement. This can be especially valuable in these fields where users are often seeking novel and immersive experiences.
Evaluation and Results
The proposed approach was evaluated on two datasets: LastFM listening histories and synthetic Netflix viewing profiles. The results showed that reranking items based on complex network metrics led to a more unexpected and surprising composition of recommendation lists. This indicates that the incorporation of network-level metrics can indeed influence the degree of surprise in recommendations, providing users with a more diverse and engaging set of suggestions.
In conclusion, the addition of a knowledge graph-based layer on top of existing recommender systems can enhance the surprise factor in recommendations. By leveraging the structural properties of the graph and incorporating complex network metrics, the proposed approach provides users with a more diverse and unexpected set of recommendations. This has wide-ranging applications in multimedia information systems, animations, artificial reality, augmented reality, and virtual realities, enhancing user experiences and expanding their horizons.