论文标题
多目标推荐系统：调查和挑战
Multi-Objective Recommender Systems: Survey and Challenges
论文作者
论文摘要
推荐系统可以被描述为软件解决方案，可为用户提供方便地访问相关内容。传统上，推荐系统研究主要集中于开发旨在预测哪些内容与单个用户相关的机器学习算法。但是，在实际应用中，在许多情况下，优化相关预测作为一个目标的准确性是不够的。取而代之的是，必须考虑多个且经常竞争的目标，从而导致需要对多目标推荐系统进行更多研究。我们可以区分几种类型的竞争目标，包括（i）在个人和总级别上的竞争建议质量目标，（ii）不同涉及的利益相关者的竞争目标，（iii）长期与短期目标，（iv）在用户界面级别的目标，以及（v）系统级别的目标目标。在本文中，我们回顾了这些类型的多目标推荐设置，并概述了该领域的开放挑战。
Recommender systems can be characterized as software solutions that provide users convenient access to relevant content. Traditionally, recommender systems research predominantly focuses on developing machine learning algorithms that aim to predict which content is relevant for individual users. In real-world applications, however, optimizing the accuracy of such relevance predictions as a single objective in many cases is not sufficient. Instead, multiple and often competing objectives have to be considered, leading to a need for more research in multi-objective recommender systems. We can differentiate between several types of such competing goals, including (i) competing recommendation quality objectives at the individual and aggregate level, (ii) competing objectives of different involved stakeholders, (iii) long-term vs. short-term objectives, (iv) objectives at the user interface level, and (v) system level objectives. In this paper we review these types of multi-objective recommendation settings and outline open challenges in this area.
