Recommender systems are used in many different applications and contexts, however their main goal can always be summarised as "connecting relevant content to interested users". Personalized recommendation algorithms achieve this goal by first building a profile of the user, either implicitly or explicitly, and then matching items with this profile to find relevant content. The more interpretable the profile and this "matching function" are, the easier it is to provide users with accurate and intuitive explanations, and also to let them interact with the system. Indeed, for a user to see what the system has already learned about her interests is of key importance for her to provide feedback to the system and to guide it towards better understanding her preferences. To this end, we propose a linear collaborative filtering recommendation model that builds user profiles within the domain of item metadata, which is arguably the most interpretable domain for end users. Our method is hence inherently transparent and explainable. Moreover, since recommendations are computed as a linear function of item metadata and the interpretable user profile, our method seamlessly supports interactive recommendation. In other words, users can directly tweak the weights of the learned profile for more fine-grained browsing and discovery of content based on their current interests. We demonstrate the interactive aspect of this model in an online application for discovering cultural events in Belgium. Additionally, the performance of the model is evaluated with offline experiments, both static and with simulated feedback, and compared to several state-of-the-art and state-of-practice baselines.
翻译:建议系统被用于许多不同的应用程序和背景,但其主要目标总可以被概括为“将相关内容与感兴趣的用户连接到相关内容”。个性化建议算法通过首先以隐含或明确的方式建立用户配置,然后将项目与该配置匹配,以找到相关内容。对配置和“匹配功能”的解释越多,就越容易向用户提供准确和直观的解释,也越容易让他们与系统互动。事实上,用户要了解系统已经了解的关于她利益的内容,她就必须向系统提供反馈,并指导系统更好地了解她的偏好,个人化建议算法就能够实现这一目标。为此,我们提议了一个线性协作过滤建议模型,在项目元数据领域建立用户配置的用户配置,这是对终端用户最可解释的领域。因此,我们的方法就具有内在的透明度和解释性能。此外,由于建议是作为项目元数据的线性功能和可解释的用户配置,因此我们的方法天衣无缝地支持互动式建议。换句话说,用户可以直接将所学的配置配置配置配置配置配置的配置的系统,从而更好地了解她的偏好地理解她的偏好选择。为此,在比利时的模拟实验中,我们以演示的模型和在线实验中,在模拟实验中,我们以演示的演示的演示的演示的实验中,在网上的演示的演示的演示的演示的演示的实验中进行。