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.

本研究在已有的推荐系统基础上，提出了一种基于知识图谱的推荐系统，通过编码用户对物品目录的交互来评估并增加推荐的出人意料程度。实验结果表明，基于复杂网络度量重新排序物品可以产生更出人意料和令人惊喜的推荐列表。