Recommender systems serve a dual purpose for users: sifting out inappropriate or mismatched information while accurately identifying items that align with their preferences. Numerous recommendation algorithms are designed to provide users with a personalized array of information tailored to their preferences. Nevertheless, excessive personalization can confine users within a "filter bubble". Consequently, achieving the right balance between accuracy and diversity in recommendations is a pressing concern. To address this challenge, exemplified by music recommendation, we introduce the Diversified Weighted Hypergraph music Recommendation algorithm (DWHRec). In the DWHRec algorithm, the initial connections between users and listened tracks are represented by a weighted hypergraph. Simultaneously, associations between artists, albums and tags with tracks are also appended to the hypergraph. To explore users' latent preferences, a hypergraph-based random walk embedding method is applied to the constructed hypergraph. In our investigation, accuracy is gauged by the alignment between the user and the track, whereas the array of recommended track types measures diversity. We rigorously compared DWHRec against seven state-of-the-art recommendation algorithms using two real-world music datasets. The experimental results validate DWHRec as a solution that adeptly harmonizes accuracy and diversity, delivering a more enriched musical experience. Beyond music recommendation, DWHRec can be extended to cater to other scenarios with similar data structures.

翻译：推荐系统为用户承担着双重功能：筛选出不适当或不匹配的信息，同时准确识别符合其偏好的物品。众多推荐算法旨在为用户提供个性化信息阵列以满足其偏好。然而，过度个性化会使用户陷入"过滤泡"中。因此，在推荐准确性与多样性之间寻求恰当平衡成为亟待解决的问题。针对这一挑战，本文以音乐推荐为例，提出多样化加权超图音乐推荐算法（DWHRec）。该算法中，用户与已听曲目的初始关联通过加权超图表示，同时艺术家、专辑及标签与曲目的关联也被纳入超图。为挖掘用户潜在偏好，对构建的超图应用基于超图的随机游走嵌入方法。本研究中，准确性通过用户与曲目之间的契合度衡量，而多样性则由推荐曲目类型的分布范围评估。我们使用两个真实音乐数据集，将DWHRec与七种最先进的推荐算法进行严格对比。实验结果验证了DWHRec能巧妙平衡准确性与多样性，提供更丰富的音乐体验。除音乐推荐外，DWHRec可扩展应用于其他具有相似数据结构的场景。