The effectiveness of recommendation systems is pivotal to user engagement and satisfaction in online platforms. As these recommendation systems increasingly influence user choices, their evaluation transcends mere technical performance and becomes central to business success. This paper addresses the multifaceted nature of recommendations system evaluation by introducing a comprehensive suite of metrics, each tailored to capture a distinct aspect of system performance. We discuss * Similarity Metrics: to quantify the precision of content-based filtering mechanisms and assess the accuracy of collaborative filtering techniques. * Candidate Generation Metrics: to evaluate how effectively the system identifies a broad yet relevant range of items. * Predictive Metrics: to assess the accuracy of forecasted user preferences. * Ranking Metrics: to evaluate the effectiveness of the order in which recommendations are presented. * Business Metrics: to align the performance of the recommendation system with economic objectives. Our approach emphasizes the contextual application of these metrics and their interdependencies. In this paper, we identify the strengths and limitations of current evaluation practices and highlight the nuanced trade-offs that emerge when optimizing recommendation systems across different metrics. The paper concludes by proposing a framework for selecting and interpreting these metrics to not only improve system performance but also to advance business goals. This work is to aid researchers and practitioners in critically assessing recommendation systems and fosters the development of more nuanced, effective, and economically viable personalization strategies. Our code is available at GitHub - https://github.com/aryan-jadon/Evaluation-Metrics-for-Recommendation-Systems.

翻译：推荐系统的有效性对于在线平台的用户参与度和满意度至关重要。随着这些推荐系统日益影响用户选择，其评估已超越单纯的技术性能，成为商业成功的核心。本文针对推荐系统评估的多面性，引入了一套全面的评价指标，每个指标旨在捕捉系统某一特定方面的性能。我们讨论了：*相似度指标：用于量化基于内容过滤机制的精度，并评估协同过滤技术的准确性；*候选生成指标：用于评估系统识别广泛且相关项目集合的有效性；*预测指标：用于衡量预测用户偏好的准确性；*排序指标：用于评估推荐呈现顺序的有效性；*商业指标：用于将推荐系统的性能与经济目标对齐。我们的方法强调这些指标的情境化应用及其相互依赖性。在本文中，我们识别了当前评估实践的优势与局限，并揭示了在跨不同指标优化推荐系统时出现的微妙权衡。最后，本文提出一个选择与解读这些指标的框架，旨在不仅提升系统性能，还能推动商业目标。本工作旨在帮助研究人员与实践者批判性地评估推荐系统，并促进开发更细致、更有效且经济可行的个性化策略。我们的代码可在GitHub获取：https://github.com/aryan-jadon/Evaluation-Metrics-for-Recommendation-Systems。