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.

翻译：随着网络作为电子与商业交易媒介的兴起，推荐系统的重要性日益凸显。该技术发展的关键驱动力之一，在于用户能够通过简单的鼠标点击轻松提供关于个人喜好的反馈。此类反馈通常以评分形式收集，但也可从用户的浏览与购买历史中推断得出。推荐系统利用用户历史数据推断客户兴趣，并提供个性化推荐。推荐的基本原理在于：以用户和物品为中心的活动之间存在显著关联性，可通过数据驱动的方式学习这些关联以进行精准预测。协同过滤是一类推荐算法，其利用多用户评分预测缺失评分，或利用二元点击信息预测潜在点击行为。然而，推荐系统可更为复杂，并能整合基于内容的属性、用户交互及上下文信息等辅助数据。