Loan Recommendation in P2P Lending Investment Networks: A Hybrid Graph Convolution Approach Low successful rate of loan money has become a significant challenge for P2P Lending platforms. In this paper, we propose a hybrid deep learning algorithm combining strengths of both supervised graph convolution network and unsupervised community discovery, to accurately match the loan requirements and investing lenders on P2P Lending platforms. Our hybrid deep architecture learns predictive node embedding from both local neighborhood information and global structural information through complex investment networks with minimal information loss. We evaluate our method on largescale dataset collected from a realworld P2P platform. Compared with strong baselines, our proposed method provides optimal loan recommendation performance, generates efficient solutions for xe2x80x9cColdStartxe2x80x9d problem and features fast computing speed.

Multigraph Convolution Collaborative Filtering Personalized recommendation is ubiquitous, playing an important role in many online services. Substantial research has been dedicated to learning vector representations of users and items with the goal of predicting a useru0027s preference for an item based on the similarity of the representations. Techniques range from classic matrix factorization to more recent deep learning based methods. However, we argue that existing methods do not make full use of the information that is available from useritem interaction data and the similarities between user pairs and item pairs. In this work, we develop a graph convolutionbased recommendation framework, named MultiGraph Convolution Collaborative Filtering (MultiGCCF), which explicitly incorporates multiple graphs in the embedding learning process. MultiGCCF not only expressively models the highorder information via a bipartite useritem interaction graph, but integrates the proximal information by building and processing useruser and itemitem graphs. Furthermore, we consider the intrinsic difference between user nodes and item nodes when performing graph convolution on the bipartite graph. We conduct extensive experiments on four publicly accessible benchmarks, showing significant improvements relative to several stateoftheart collaborative filtering and graph neural networkbased recommendation models. Further experiments quantitatively verify the effectiveness of each component of our proposed model and demonstrate that the learned embeddings capture the important relationship structure.