  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.
