Exploring the Impact of Temporal Bias in Point-of-Interest Recommendation

TL;DR

This paper investigates how temporal bias in user check-in times affects the fairness of POI recommendation systems, revealing that current models favor certain user groups based on time, which could impact traffic and pollution.

Contribution

It introduces the concept of temporal bias in POI recommendations and evaluates its impact on model fairness using real datasets, highlighting a previously overlooked issue.

Findings

Models prefer users based on check-in time, affecting fairness.

Temporal bias persists even with equal user interactions.

Addressing bias could reduce traffic congestion and pollution.

Abstract

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.