Event-based Social Networks (EBSN), combining online networks with offline users, provide versatile event recommendations for offline users through complex social networks. However, there are some issues that need to be solved in EBSN: (1) The online static data could not satisfy the online dynamic recommendation demand; (2) the implicit behavior information tends to be ignored, reducing the accuracy of the recommendation algorithm; and (3) the online recommendation description is inconsistent with the offline activity. To address these issues, an Incentive Improved DQN (IIDQN) based on Deep Q-Learning Networks (DQN) is proposed. More specifically, we introduce the agents to interact with the environment through online dynamic data. Furthermore, we consider two types of implicit behavior information: the length of the user’s browsing time and the user’s implicit behavior factors. As for the problem of inconsistency, based on blockchain technology, a new activities event approach on EBSN is proposed, where all activities are recorded on the chain. Finally, the simulation results indicate that the IIDQN algorithm greatly outperforms in mean rewards and recommendation performance than before DQN.

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Payment channel networks (PCNs) are generally regarded as one of the most effective and promising scalability solutions for blockchain-based cryptocurrency systems, but suffer the issues of low success ratio and long confirmation latency in processing transactions. In this paper, we demonstrate the feasibility of tremendously increasing the success ratio of transactions and improving their execution efficiency by enhancing network nodes' connectivity and enforcing a balanced network channel capacity. To implement such ideas, multiple designs have been made. First, to extent nodes connectivity, we transform the nearlylinear ordered nodes into a star payment structured typology, and design an incentive financing mechanism to restructure a new landmark routing typology design. Especially, for the marginalized or dissociative nodes, we utilize specific financial loan strategies to encourage them to (re)join the system. Besides, we propose the Power Atomic Multi-Path Payments (Power AMP) traffic distribution method, which hierarchically allocates the bottleneck's currentlyavailable capacity (to replace the random or equal division used in traditional AMP), and thus archives a balanced traffic usage. With such efforts, we improve the transaction success ratio and efficiency of