CBRec: A causal way balancing multidimensional attraction effect in POI recommendations

Abstract

In the next Point-of-Interest recommendation, sparse and uneven location data generate biases, resulting in homogeneous recommendation outcomes that fail to reflect user preferences. Although there are many related unbiased studies, they still exhibit limitations. They lack a unified debiasing paradigm and typically employ different methods to address various biases, resulting in complex and incompatible debiasing models. Additionally, they often overlook the potential advantages of biases, thus harming the quality of location features. To address these challenges, we propose a unified debiasing paradigm by intervening in location attraction to balance the positive and negative effects of bias. By analyzing the structural causal graph, we identify attraction as a feature influenced by bias. By comparing observational results affected by attraction with counterfactual results unaffected by it, we derive a unified debiasing paradigm that eliminates the effects of bias. Additionally, through feature fusion, we embed multidimensional attraction into user features, leveraging the advantages of bias to preserve the quality of location features. Finally, experimental results on five real-world datasets demonstrate that our proposed model outperforms recent sequential recommendation models.