Beyond Relevance: An Adaptive Exploration-Based Framework for Personalized Recommendations

• URL: http://arxiv.org/abs/2503.19525v1
• Date: Tue, 25 Mar 2025 10:27:32 GMT
• Title: Beyond Relevance: An Adaptive Exploration-Based Framework for Personalized Recommendations
• Authors: Edoardo Bianchi,
• Abstract summary: This paper introduces an exploration-based recommendation framework to promote diversity and novelty without compromising relevance.<n>A user-controlled exploration mechanism enhances diversity by selectively sampling from under-explored clusters.<n>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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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