Dynamic Offset Metric on Heterogeneous Information Networks for Cold-start Recommendation

The cold-start problem poses a significant challenge in recommendation systems, particularly when interaction data is scarce. While meta-learning has shown promise in few-shot classification, its application to cold-start recommendations has mostly involved simple transplantations of generic approaches. The effectiveness of metric learning, a powerful meta-learning method, is hindered by differences in problem definition when applied to rating prediction. Heterogeneous information networks (HINs), as high-order graph structures, can capture valuable semantic information even in data-starved conditions. Efficient utilization of HINs can alleviate the cold-start dilemma. However, in the cold-start domain, there is a lack of dynamic node-level and semantic-level feature fusion schemes, resulting in the underutilization of complex information. This study addresses these issues by combining metric learning and HINs, proposing OMHIN (Dynamic Offset Metric approach to Heterogeneous Information Networks). Our approach transforms a direct similarity metric into an indirect metric to enhance model robustness. By flexibly applying one-dimensional convolution, OMHIN effectively integrates rich information from HINs while minimizing noise introduction. Experimental results on two datasets demonstrate that OMHIN achieves state-of-the-art performance in various scenarios, particularly in complex and challenging situations. It is especially suitable for sequence cold-start recommendations.