剖析推荐系统评估的理论基础，揭示如何科学比较不同推荐模型。
Dissertation: On the Theoretical Foundation of Model Comparison and Evaluation for Recommender System
- 从用户-物品交互中挖掘依赖关系，构建数据驱动的推荐机制。
- 提出系统性框架，解决推荐模型评估中的可比性与一致性难题。
- 适合研究推荐算法评测或构建可信评估体系的研究者。
随着网络成为电子交易的重要媒介，推荐系统的重要性日益凸显。用户通过简单点击即可提供偏好反馈，这些反馈常以评分形式收集，也可从浏览和购买历史中推断。推荐系统利用用户的过往行为数据，推断其兴趣并提供个性化推荐。其核心原理是用户与物品间的活动存在显著依赖关系，可通过数据驱动方式学习，实现精准预测。协同过滤是一类推荐算法，利用多用户评分预测缺失评分，或用二进制点击信息预测潜在点击。然而，推荐系统可能更复杂，可融合内容属性、用户交互及上下文信息等辅助数据。
原文摘要 · Abstract (English)
Recommender systems have become increasingly important with the rise of the web as a medium for electronic and business transactions. One of the key drivers of this technology is the ease with which users can provide feedback about their likes and dislikes through simple clicks of a mouse. This feedback is commonly collected in the form of ratings, but can also be inferred from a user's browsing and purchasing history. Recommender systems utilize users' historical data to infer customer interests and provide personalized recommendations. The basic principle of recommendations is that significant dependencies exist between user- and item-centric activity, which can be learned in a data-driven manner to make accurate predictions. Collaborative filtering is one family of recommendation algorithms that uses ratings from multiple users to predict missing ratings or uses binary click information to predict potential clicks. However, recommender systems can be more complex and incorporate auxiliary data such as content-based attributes, user interactions, and contextual information.
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