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.

翻译：传统推荐方案（包括基于内容和协同过滤）通常关注项目或用户间的相似性。现有方法缺乏在推荐中引入意外性的途径，倾向于优先推荐全局热门项目，而非让用户接触意料之外的项目。本研究旨在设计并评估一种适用于推荐系统的新增层，该层能够整合关系信息并推荐具有用户定义惊喜程度的项目。我们通过编码用户与项目目录的交互行为，提出一种基于知识图谱的推荐系统。研究探索了知识图谱上的网络级指标能否影响推荐的惊喜程度。我们假设惊喜度与特定网络指标相关，并将用户画像视为大规模目录知识图谱中的子图。所提出的解决方案根据推荐对结构图指标的影响进行重排序。本研究为优化推荐以体现这些指标做出了贡献。我们在LastFM收听历史记录和合成Netflix观影资料两个数据集上进行了实验评估。结果表明，基于复杂网络指标对项目进行重排序可生成更具意料之外性和惊喜性的推荐列表。