When suggesting Points of Interest (PoIs) to people with autism spectrum disorders, we must take into account that they have idiosyncratic sensory aversions to noise, brightness and other features that influence the way they perceive places. Therefore, recommender systems must deal with these aspects. However, the retrieval of sensory data about PoIs is a real challenge because most geographical information servers fail to provide this data. Moreover, ad-hoc crowdsourcing campaigns do not guarantee to cover large geographical areas and lack sustainability. Thus, we investigate the extraction of sensory data about places from the consumer feedback collected by location-based services, on which people spontaneously post reviews from all over the world. Specifically, we propose a model for the extraction of sensory data from the reviews about PoIs, and its integration in recommender systems to predict item ratings by considering both user preferences and compatibility information. We tested our approach with autistic and neurotypical people by integrating it into diverse recommendation algorithms. For the test, we used a dataset built in a crowdsourcing campaign and another one extracted from TripAdvisor reviews. The results show that the algorithms obtain the highest accuracy and ranking capability when using TripAdvisor data. Moreover, by jointly using these two datasets, the algorithms further improve their performance. These results encourage the use of consumer feedback as a reliable source of information about places in the development of inclusive recommender systems.
翻译:在向有自闭症谱系障碍的人建议关注点时,我们必须考虑到,他们对噪音、亮度和其他影响其感知方式的特征有独特的感官厌恶,因此,建议系统必须处理这些方面。然而,检索关于自闭症的感官数据是一个真正的挑战,因为大多数地理信息服务器都未能提供这些数据。此外,临时热量众包运动并不能保证覆盖大片地理区域,缺乏可持续性。因此,我们调查从基于地点的服务所收集的消费者反馈中提取有关地点的感官数据,人们自发地从世界各地对这些数据进行审查。具体地说,我们提出了一个从对《自闭症审查》中提取感官数据的模型,并将这些数据纳入建议系统,通过考虑用户偏好和兼容性信息来预测项目评级。我们测试了我们与自闭症和神经典型人的方法,将它纳入不同的建议算法。测试时,我们使用了在众包运动中建立的一个数据集,以及从特里帕德审查中提取的另一种数据。结果显示,从关于从对《自闭感知》的审查中提取的感官数据中提取了一种最高的感官数据。我们建议,在使用这两种数据时,利用了两种数据来鼓励使用这些数据源,从而进一步使用这些数据。