论文标题
迈向校准的推荐信心
Towards Confidence-aware Calibrated Recommendation
论文作者
论文摘要
推荐系统利用用户的历史数据来学习和预测他们的未来兴趣，从而为他们的口味量身定制了建议。校准确保推荐项目类别的分布与用户的历史数据一致。缓解误解为推荐系统带来了各种好处。例如，系统仅通过推荐流行类别而忽略用户配置文件上交互的类别的可能性较小。尽管取得了显着的成功，但校准方法具有几个缺点，例如限制了推荐项目的多样性，而不是考虑校准置信度。这项工作提出了一组属性，这些属性解决了所需校准的推荐系统的各个方面。考虑到这些属性，我们提出了一种基于置信度优化的重新排行算法，以找到校准，相关性和项目多样性之间的平衡，同时根据用户配置文件的大小考虑了校准置信度。我们的模型在不同用户组的各种准确性和超越准确度指标方面都优于最先进的方法。
Recommender systems utilize users' historical data to learn and predict their future interests, providing them with suggestions tailored to their tastes. Calibration ensures that the distribution of recommended item categories is consistent with the user's historical data. Mitigating miscalibration brings various benefits to a recommender system. For example, it becomes less likely that a system overlooks categories with less interaction on a user's profile by only recommending popular categories. Despite the notable success, calibration methods have several drawbacks, such as limiting the diversity of the recommended items and not considering the calibration confidence. This work, presents a set of properties that address various aspects of a desired calibrated recommender system. Considering these properties, we propose a confidence-aware optimization-based re-ranking algorithm to find the balance between calibration, relevance, and item diversity, while simultaneously accounting for calibration confidence based on user profile size. Our model outperforms state-of-the-art methods in terms of various accuracy and beyond-accuracy metrics for different user groups.
