论文标题
对自闭症患者的个性化pois建议
Personalized Recommendation of PoIs to People with Autism
论文作者
论文摘要
自闭症谱系障碍（ASD）患者兴趣点的建议提出了推荐系统研究的挑战，因为这些用户对地方的看法受到特殊感觉厌恶的影响，这些感觉可以通过引起压力和焦虑来灌输他们的经历。因此，管理个人偏好不足以为这些人提供合适的建议。为了解决这个问题，我们提出了一个顶级建议模型，将用户的特质厌恶与她/他的偏好相结合，以个性化的方式建议对她/他的最兼容和最讨人喜欢的兴趣点。我们有兴趣在推荐模型中找到兼容性和兴趣的特定用户平衡，该模型集成了异质评估标准以适当考虑这些方面。我们对ASD和“神经型”人都测试了我们的模型。评估结果表明，在这两个组上，我们的模型在准确性和排名能力上都优于基于项目兼容性，用户偏好或通过统一评估模型整合这两个方面的推荐系统。
The suggestion of Points of Interest to people with Autism Spectrum Disorder (ASD) challenges recommender systems research because these users' perception of places is influenced by idiosyncratic sensory aversions which can mine their experience by causing stress and anxiety. Therefore, managing individual preferences is not enough to provide these people with suitable recommendations. In order to address this issue, we propose a Top-N recommendation model that combines the user's idiosyncratic aversions with her/his preferences in a personalized way to suggest the most compatible and likable Points of Interest for her/him. We are interested in finding a user-specific balance of compatibility and interest within a recommendation model that integrates heterogeneous evaluation criteria to appropriately take these aspects into account. We tested our model on both ASD and "neurotypical" people. The evaluation results show that, on both groups, our model outperforms in accuracy and ranking capability the recommender systems based on item compatibility, on user preferences, or which integrate these two aspects by means of a uniform evaluation model.
