Internet insurance products are apparently different from traditional e-commerce goods for their complexity, low purchasing frequency, etc. So, cold start problem is even worse. In traditional e-commerce field, several cross-domain recommendation (CDR) methods have been studied to infer preferences of cold start users based on their preferences in other domains. However, these CDR methods couldnâĂŹt be applied into insurance domain directly due to product complexity. In this paper, we propose a Deep Cross-Domain Insurance Recommendation System (DCDIR) for cold start users. Specifically, we first learn more effective user and item latent features in both domains. In target domain, given the complexity of insurance products, we design a meta-path based method over insurance product knowledge graph. In source domain, we employ GRU to model users' dynamic interests. Then we learn a feature mapping function by multi-layer perceptions. We apply DCDIR on our companyâĂŹs dataset, and show DCDIR significantly outperforms the state-of-the-art solutions. CCS CONCEPTS • Applied computing → Online insurance; • Information systems → Data mining; • Networks → Data path algorithms.

Internet is changing the world, adapting to the trend of internet sales will bring revenue to traditional insurance companies. Online insurance is still in its early stages of development, where cold start problem (prospective customer) is one of the greatest challenges. In traditional e-commerce field, several cross-domain recommendation (CDR) methods have been studied to infer preferences of cold start users based on their preferences in other domains. However, these CDR methods couldn't be applied to insurance domain directly due to the domain's specific properties. In this paper, we propose a novel framework called a Heterogeneous information network based Cross Domain Insurance Recommendation (HCDIR) system for cold start users. Specifically, we first try to learn more effective user and item latent features in both source and target domains. In source domain, we employ gated recurrent unit (GRU) to module users' dynamic interests. In target domain, given the complexity of insurance products and the data sparsity problem, we construct an insurance heterogeneous information network (IHIN) based on data from PingAn Jinguanjia, the IHIN connects users, agents, insurance products and insurance product properties together, giving us richer information. Then we employ three-level (relational, node, and semantic) attention aggregations to get user and insurance product representations. After obtaining latent features of overlapping users, a feature mapping between the two domains is learned by multi-layer perceptron (MLP). We apply HCDIR on Jinguanjia dataset, and show HCDIR significantly outperforms the state-of-the-art solutions. CCS CONCEPTS • Applied computing → Online insurance; • Information systems → Data mining; • Networks → Data path algorithms.

The present study aimed to identify the mechanism of tactile sensation by analyzing the regularity of the firing pattern of Merkel cell-neurite complex (MCNC) under the stimulation of different compression depths. The fingertips were exposed to the contact pressure of a spherical object to sense external stimuli in this study. The distribution structure of slowly adapting type I (SAI) mechanoreceptors was considered for analyzing the neural coding of tactile stimuli, especially the firing pattern of SAI neural network for perceiving the external stimulation. The numerical simulation results showed that (1) when the skin was pressed by the same sphere and the depth of the pressing finger skin and position of the force application point remained unchanged, the firing rate of the neuron depended on the synergistic effect of the number of receptors connected with the neuron and the distance between the neuron and the force application point. (2) When the fingertip was pressed by the same sphere at a constant depth and the different contact position, the overall firing rate of the MCNC neural network increased with the number of SAI mechanoreceptors in the area where the force application point was located.

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