Recommending appropriate travel destinations to consumers based on contextual information such as their check-in time and location is a primary objective of Point-of-Interest (POI) recommender systems. However, the issue of contextual bias (i.e., how much consumers prefer one situation over another) has received little attention from the research community. This paper examines the effect of temporal bias, defined as the difference between users' check-in hours, leisure vs.~work hours, on the consumer-side fairness of context-aware recommendation algorithms. We believe that eliminating this type of temporal (and geographical) bias might contribute to a drop in traffic-related air pollution, noting that rush-hour traffic may be more congested. To surface effective POI recommendations, we evaluated the sensitivity of state-of-the-art context-aware models to the temporal bias contained in users' check-in activities on two POI datasets, namely Gowalla and Yelp. The findings show that the examined context-aware recommendation models prefer one group of users over another based on the time of check-in and that this preference persists even when users have the same amount of interactions.
翻译:根据背景信息,如客户的报到时间和地点,向消费者建议适当的旅行目的地,是利益点建议系统的首要目标。然而,背景偏差问题(即消费者多喜欢一种情况而不是另一种情况)很少受到研究界的关注。本文审视了时间偏差的影响,即用户的报到时间、闲暇时间与工作小时之间的差别,对了解背景建议算法的消费者对消费者的公平性的影响。我们认为,消除这种时间(和地理)偏差可能会导致与交通有关的空气污染减少,我们指出,匆忙时的交通可能更加拥挤。为了浮现有效的POI建议,我们评估了最先进的背景认识模型对用户在两次POI数据组,即Govala和Yelp的报到时活动中包含的时间偏差的敏感性。调查结果显示,根据检查背景的推荐模式,根据检查时间,对用户的一组比另一组更喜欢,即使用户有相同程度的互动,这种偏好依然存在。