根据用户好奇心动态调整推荐中实用与意外的平衡。
A Serendipitous Recommendation System Considering User Curiosity
- 结合长期与短期兴趣估计用户好奇心，动态调节推荐偏好。
- 在MovieLens-1M上达到顶尖方法的准确率水平。
- 适合希望提升推荐多样性与惊喜感的研究者和产品设计者。
为解决过度追求预测准确导致推荐范围狭窄的问题，兼具有用性与意外性的偶然推荐受到关注。然而，由于不同用户对有用性与意外性的偏好比例各异，且受其知识渴求程度影响，实现偶然推荐颇具挑战。本文提出一种基于用户好奇心估计其偏好的方法，以匹配个性化需求。通过融合用户的长期与短期兴趣来推断好奇心，并据此生成推荐。在MovieLens-1M数据集上进行离线实验，结果表明，该方法在保持与当前最优方法相当性能的同时，成功实现偶然推荐。
原文摘要 · Abstract (English)
To address the problem of narrow recommendation ranges caused by an emphasis on prediction accuracy, serendipitous recommendations, which consider both usefulness and unexpectedness, have attracted attention. However, realizing serendipitous recommendations is challenging due to the varying proportions of usefulness and unexpectedness preferred by different users, which is influenced by their differing desires for knowledge. In this paper, we propose a method to estimate the proportion of usefulness and unexpectedness that each user desires based on their curiosity, and make recommendations that match this preference. The proposed method estimates a user's curiosity by considering both their long-term and short-term interests. Offline experiments were conducted using the MovieLens-1M dataset to evaluate the effectiveness of the proposed method. The experimental results demonstrate that our method achieves the same level of performance as state-of-the-art method while successfully providing serendipitous recommendations.
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