A Recommendation System is a sub-type of an information filtering system that is used to predict/select the most relevant items for a user given a goa

推荐系统是信息过滤系统的子类型，用于预测/选择给定目标和潜在上下文的用户最相关的项目。例如，安迪想在周五晚上看一部喜剧。在这个例子中，目标是找到一部安迪会喜欢的喜剧，背景是星期五晚上。那么，一个相关的问题是：我们的系统做得有多好？长期以来，相关工作一直专注于衡量RSS的有效性，大量研究致力于设计新的指标来衡量产生的建议的准确性，以及基于这些指标产生更准确的建议的新算法。然而，RSS也可以从另一个维度进行评估：效率，即基于在实践中培训和使用RS所需的资源。建议系统应该表现出内在的效率和有效性之间的权衡。在这个项目中，我们做了三个主要假设：(A)推荐系统的效率可以通过一系列方程来建模，这些方程封装了输入的特征、算法的属性以及施加/期望的服务质量约束/目标；(B)可以建立预测模型，以估计受预定义资源约束的推荐算法家族的效率-效果曲线；因此，在这个项目中，我们将通过绘制推荐系统的理论计算(空间/时间)复杂度、实际训练时间和资源消耗，以及通过各种质量/精度度量所获得的有效性来检验推荐系统的效率。然后，我们将提出一种方法，通过考虑推荐任务、算法的选择、可用资源、服务质量约束/目标和推荐的有效性来规划推荐系统的设计空间。拟议的方法将概括推荐系统的效率和效力，突出每种情况的优点和局限性。通过这样做，我们还希望发现设计空间中的未知领域，可能导致新的和改进的RS算法。这个项目的工作与数字经济EPSRC主题的“内容创造和消费”和“数字商业模式”优先领域一致，并正好落在信息和通信技术主题的“信息系统”研究领域。这项工作源于并加强了作为欧盟资助的PRIMES项目的一部分进行的研究，PRIMES是格拉斯哥大学、HT2实验室以及法国和荷兰的两所中学之间的合作项目。

A Recommendation System is a sub-type of an information filtering system that is used to predict/select the most relevant items for a user given a goal, and potentially a context. For example, Andy would like to watch a comedy on Friday night. In this example, the goal is to find a comedy that Andy would enjoy, and the context is Friday night. A pertinent question then is: how well did our system do? Related work has long focused on measuring the effectiveness of RSs, with a big body of research devoted to devising novel metrics to measure the accuracy of produced recommendations and novel algorithms that produce more accurate recommendations based on these metrics. However, RSs can also be evaluated along another dimension: efficiency; that is, based on the resources required to train and to use a RS in practice.Recommendation systems are expected to exhibit inherent efficiency-effectiveness trade-offs. In this project we thus make three main hypotheses: (a) The efficiency of a recommendation system can be modelled through a series of equations that encapsulate characteristics of the input, properties of the algorithms, and what quality of service constraints/goals are imposed/expected; (b) A predictive model can be built so as to estimate the efficiency-effectiveness curve for families of recommendation algorithms subject to predefined resource constraints; and (c) A methodology can be devised to allow arbitrary RS algorithms to be cast in this efficiency-effectiveness space so as to facilitate comparisons to other related algorithms.Consequently, in this project we will examine the efficiency of recommendation systems, by mapping out their theoretical computational (space/time) complexity, actual training time and resource consumption, in tandem with the attained effectiveness quantified through various quality/accuracy metrics. Then, we will propose a methodology that maps out the design space of recommendation systems, by taking into account the recommendation task, the choice of algorithms, the available resources, the quality of service constraints/goals, and the effectiveness of the recommendations. The proposed methodology will encapsulate the efficiency-effectiveness of recommendation systems, highlighting the advantages and limitations in each case. In doing so, we also expect to uncover uncharted territories in the design space, possibly leading to new and improved RS algorithms.The work in this project aligns with the "Content Creation and Consumption" and "Digital Business Models" priority areas of the Digital Economy EPSRC theme, and falls squarely in the "Information Systems" research area of the Information and Communication Technologies theme. This work stems from and augments research carried out as part of the EU-funded project PRIMES -- a collaboration among the University of Glasgow, HT2 Labs, and two secondary schools from France and the Netherlands.