通过自适应探索提升推荐多样性，兼顾个性化与新颖性。
Beyond Relevance: An Adaptive Exploration-Based Framework for Personalized Recommendations
- 基于语义聚类与可调阈值算法动态组织物品
- 探索机制使列表相似度降至0.26，意外度达0.73
- 适合关注长期用户偏好与推荐多样性的研究者
推荐系统需在个性化、多样性与冷启动鲁棒性之间取得平衡。本文提出一种自适应探索式推荐框架，通过句子嵌入表示物品，并采用在线算法结合自适应阈值构建语义一致的聚类结构。用户可控的探索机制通过有选择地采样低曝光聚类来增强多样性。在MovieLens数据集上的实验表明，启用探索后，列表内相似度从0.34降至0.26，意外度提升至0.73，优于协同过滤与流行度基线。对300名模拟用户的A/B测试显示，72.7%的长期用户偏好探索型推荐。计算分析表明，聚类与推荐过程随聚类数量线性扩展。结果证明，该框架能有效缓解过度专业化问题，同时保持个性化与高效性。
原文摘要 · Abstract (English)
Recommender systems must balance personalization, diversity, and robustness to cold-start scenarios to remain effective in dynamic content environments. This paper introduces an adaptive, exploration-based recommendation framework that adjusts to evolving user preferences and content distributions to promote diversity and novelty without compromising relevance. The system represents items using sentence-transformer embeddings and organizes them into semantically coherent clusters through an online algorithm with adaptive thresholding. A user-controlled exploration mechanism enhances diversity by selectively sampling from under-explored clusters. Experiments on the MovieLens dataset show that enabling exploration reduces intra-list similarity from 0.34 to 0.26 and increases unexpectedness to 0.73, outperforming collaborative filtering and popularity-based baselines. A/B testing with 300 simulated users reveals a strong link between interaction history and preference for diversity, with 72.7% of long-term users favoring exploratory recommendations. Computational analysis confirms that clustering and recommendation processes scale linearly with the number of clusters. These results demonstrate that adaptive exploration effectively mitigates over-specialization while preserving personalization and efficiency.
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