The suggestion of Points of Interest (PoIs) to people with autism spectrum disorders challenges the research about recommender systems by introducing an explicit need to consider both user preferences and aversions in item evaluation. The reason is that autistic users' perception of places is influenced by sensory aversions, which can cause stress and anxiety when they visit the suggested PoIs. Therefore, the management of individual preferences is not enough to provide these people with suitable recommendations.
To address this issue, we propose a Top-N recommendation model that combines information about the user's idiosyncratic aversions with her/his preferences in a personalized way. The goal is that of suggesting the places that (s)he can like and smoothly experience at the same time. 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 148 adults, 20 of which were people with autism spectrum disorders. The evaluation results show that, on both groups, our model achieves superior accuracy and ranking results than the recommender systems based on item compatibility, on user preferences, or which integrate these aspects using a uniform evaluation model. These findings encourage us to use our model as a basis for the development of inclusive recommender systems.
The personalized suggestion of Points of Interest (PoIs) to fragile users challenges the development of recommender systems19 by broadening the factors to be taken into account in the identification of the most suitable items for the individual user. For instance, people with autism spectrum disorders, who are the main target of this work, have idiosyncratic sensory aversions to noise, brightness, and other sensory features, which influence the way they perceive items, especially places.20 Thus, a recommender system that overlooks these aversions could suggest PoIs that cause a high level of stress and anxiety on the user.7 In order to address this issue, the preference data traditionally used to personalize item recommendation should be combined with information about people's aversions to estimate the likelihood that, rather than only being interested in exploring the suggested places, they can serenely experience them.
Starting from Multi-Criteria Decision Analysis,25 which provides techniques for the evaluation of multiple dimensions of items, and on match-making models based on user-to-item similarity,12 most recommender systems assume that the attributes of an item contribute to its utility to the user in an additive way. However, depending on individual idiosyncrasies and their strength, problematic features might make an item unsuitable, even though it meets the user's preferences. Moreover, the impact of compatibility on decision-making varies individually and it cannot be separately managed. For instance, some people with autism are determined to visit noisy and crowded places if they like them very much. Therefore, inclusive recommendation models must reflect personal evaluation criteria by balancing feature compatibility and preference satisfaction at the individual level. In the present work, we investigate the role of these two types of information in the personalized suggestion of PoIs to users with, or without, autism spectrum disorders (neurotypical users). We propose a novel Top-N recommender system that applies heterogeneous evaluation criteria to take user preferences and compatibility requirements into account, by exploiting feature-based user profiles for the specification of individual needs.