Point-of-Interest Recommendation based on Geographical Influence and Extended Pairwise Ranking
Chang Su, Jin Wang, Xianzhong Xie · 2020
In recent years, recommendation based on explicit feedback data has been extensively studied. However, in the field of Point-of-Interest (POI) recommendation, check-in information is usually implicit feedback, that is, we can only observe positive data where users interact with POIs. The lack of negative samples brings difficulties to the research of POI recommendation. Although there have been recently studies converted the rating prediction into the POIs ranking by constructing pairwise preference assumption, they only consider the optimization of the ranking of one POI pair, which the value of negative data is underutilized. In addition, the geographical influence has not been fully utilized. Hence, we propose a recommendation model based on geographic influence and extended pairwise ranking (GIEPR). Extensive empirical studies on two publicly available datasets show that our method performs significantly better than state-of-the-art methods for POI recommendation.