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.

翻译：推荐系统必须在动态内容环境中平衡个性化、多样性和对冷启动场景的鲁棒性，以保持其有效性。本文提出了一种自适应的、基于探索的推荐框架，该框架能够适应不断演变的用户偏好和内容分布，从而在不牺牲相关性的前提下促进多样性和新颖性。该系统使用句子Transformer嵌入表示项目，并通过一种带有自适应阈值的在线算法将其组织成语义连贯的聚类。一种用户可控的探索机制通过从探索不足的聚类中有选择地采样来增强多样性。在MovieLens数据集上的实验表明，启用探索功能可将列表内相似度从0.34降低至0.26，并将意外性提升至0.73，其性能优于协同过滤和基于流行度的基线方法。对300名模拟用户进行的A/B测试揭示了交互历史与对多样性偏好之间的强关联，72.7%的长期用户更青睐探索性推荐。计算分析证实，聚类和推荐过程的计算复杂度随聚类数量呈线性增长。这些结果表明，自适应探索在保持个性化和效率的同时，能有效缓解过度专业化问题。