Collaborative filtering based recommender systems have been extremely successful in settings where user preference data on items is abundant. However, collaborative filtering algorithms are hindered by their weakness against the item cold-start problem and general lack of interpretability. Ontology-based recommender systems exploit hierarchical organizations of users and items to enhance browsing, recommendation, and profile construction. While ontology-based approaches address the shortcomings of their collaborative filtering counterparts, ontological organizations of items can be difficult to obtain when items mostly belong to the same category and are mostly alike (e.g. television series episodes). In this paper, we present an ontology-based recommender system that integrates the knowledge represented in a large ontology of literary themes to produce fiction content recommendations. The main novelty of this work is an ontology-based method for computing similarities between items and its integration with the classical Item-KNN algorithm. As study case, we evaluated the proposed method against other approaches by performing the classical rating prediction task on a collection of Star Trek television series episodes in an item cold-start scenario. This transverse evaluation provides insights into the utility of different information resources and methods for the initial stages of recommender system development. We observed that our proposed method showed to be a convenient alternative to collaborative filtering approaches for collections of mostly similar items, particularly when other content-based approaches are not applicable or otherwise unavailable. Aside from the new methods, this paper contributes a testbed for future research and an online framework to collaboratively extend the ontology of literary themes to cover other narrative content.
翻译:以协作过滤为基础的建议系统在用户偏好项目数据丰富的情况下非常成功;然而,协作过滤算法由于在项目冷启动问题和普遍缺乏解释性等方面存在薄弱之处而受阻; 内科建议系统利用用户的等级组织和项目加强浏览、建议和配置构建; 以本科为基础的方法处理其合作过滤对应方的缺点,但如果项目大多属于同一类别,而且大多相似(例如电视系列片段),则本科性项目组织可能难以获得。 在本文件中,我们提出了一个基于本科性的建议系统,将大量文学主题的本科性知识综合起来,以产生虚构的内容建议; 本工作的主要新颖之处是利用本科性方法计算项目之间的相似之处及其与经典的本科性项目-KNNN算法的整合。 作为研究案例,我们评估了拟议方法与其他方法相比,通过在项目冷启动时收集基于Star Trek电视系列的经典评级预测任务(例如电视系列片段)。 我们的横向评价提供了一个基于本科性的建议系统,而不是基于大量本科性研究主题的研究主题, 将一个应用的方法用于其他测试方法,我们所观察到的类似的合作范围,在初始阶段,我们所观察到的其他实验性研究方法是用于其他测试方法。