PCDe: A personalized conversational debiasing framework for next POI recommendation with uncertain check-ins

In the next point-of-interest (POI) recommendation, users may visit individual POIs within larger gathering places, such as shopping malls (termed as collective POIs), leading to uncertain check-ins. Our data analysis unveils that (1) the presence of such uncertain check-ins raises a new type of bias, termed as scale bias, that is, the recommender tends to recommend collective POIs over individual POIs, which further exacerbates the commonly-observed popularity bias, that is, the recommender tends to recommend popular POIs rather than unpopular ones; and (2) the existence of the above two types of biases significantly affects the fairness of next POI recommendation with uncertain check-ins. Therefore, we propose a Personalized Conversational Debiasing framework (PCDe) by exploiting the advantages of conversational techniques to capture personalized dynamic user preferences, thereby mitigating both scale and popularity biases at a personalized level. Specifically, the inquiry component designs an improved question-and-answer manner based on personalized information entropy, thus mitigating the scale bias. The rewarding component then introduces a novel debiasing reward mechanism based on the Jensen-Shannon divergence to make the recommendations better aligned with users' historical preferences on popularity, thereby addressing the popularity bias. Extensive experiments demonstrate the superiority of our proposed PCDe over state-of-the-arts (SOTAs) regarding mitigating scale and popularity biases while enhancing recommendation accuracy thanks to its personalized debiasing mechanism.