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.
翻译:传统上,建议系统研究的重点主要是开发机器学习算法,以预测哪些内容与个别用户相关。然而,在现实应用中,在许多情况下,将这种相关性预测作为单一目标的准确性最大化是不够的。相反,必须考虑多重而且往往相互竞争的目标,从而需要更多地研究多目标建议系统。我们可以区分几种相互竞争的目标,包括:(一) 个别和合计一级相互竞争的建议质量目标,(二) 不同参与的利益攸关方相互竞争的目标,(三) 长期目标与短期目标,(四) 用户界面一级的目标,(五) 系统一级的目标。在本文件中,我们审查这些多目标建议设置,并概述该领域的公开挑战。