IIDQN: An Incentive Improved DQN Algorithm in EBSN Recommender System

Abstract

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.

Blockchain‐based secure deduplication of encrypted data supporting client‐side semantically secure encryption without trusted third party

“…Different from the traditional credit endorsement mechanism relying on the trusted third party, blockchain technology is deeply integrated through P2P network, cryptography technology, consensus mechanism, etc., to solve the problem of establishing a trust mechanism between nodes in a decentralized system. 20,21 Therefore, based on the blockchain, our deduplication protocol of encrypted data solves the third-party trust mechanism problem. In our protocol, smart contracts are used to implement the proof of data ownership.…”

“…Blockchain is still considered as an effective technology to build trust in distributed nodes. 21,29,30 In 1994, the concept of smart contract was first proposed by Szabo 31 which is defined as a kind of computer protocol that executes contract terms through information technology. Blockchain can supply a platform for supporting the automatical operation of programmable smart contracts by consensus nodes without any trusted third party once predefined rules have been met.…”

“…Based on the advantages of blockchain, 19 our protocol supports client‐side semantically secure encryption without the trusted third party and does not need other uploaders to be online. Different from the traditional credit endorsement mechanism relying on the trusted third party, blockchain technology is deeply integrated through P2P network, cryptography technology, consensus mechanism, etc., to solve the problem of establishing a trust mechanism between nodes in a decentralized system 20,21 . Therefore, based on the blockchain, our deduplication protocol of encrypted data solves the third‐party trust mechanism problem.…”

Secure storage scheme of trajectory data for digital tracking mechanism

“…If it is abused by service providers without processing or stolen by third‐party malicious attackers, the user's personal sensitive information will be exposed 9 . For example, home address, workplace, health level, and even their behavior patterns, consumption ability, hobbies, social habits, and so forth will be uncovered 10,11 . According to relevant studies, 95% of people can be identified by only four spatiotemporal trajectory points 12 .…”

“…9 For example, home address, workplace, health level, and even their behavior patterns, consumption ability, hobbies, social habits, and so forth will be uncovered. 10,11 According to relevant studies, 95% of people can be identified by only four spatiotemporal trajectory points. 12 Therefore, it is crucial to ensure the secure storage of trajectory data in the digital tracking process.…”