GreenCommute: An Influence-Aware Persuasive Recommendation Approach for Public-Friendly Commute Options

Wu, Shiqing; Bai, Quan; Sengvong, Sotsay

doi:10.1007/s11518-018-5368-6

Cited by 15 publications

(7 citation statements)

References 8 publications

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“…GreenCommute [41] is a recommendation system that facilitates commuters to make environmental friendly choices. The system quantifies the utility of recommendations from a user and social perspective and provides rewards to users by balancing the conflicts between the perspectives.…”

Section: Persuasive Systems For Sustainable Mobilitymentioning

confidence: 99%

“…Behavioural Change 62.9% [6,8,10,[12][13][14]16,[20][21][22][23][24][25]28,29,31,33,41] Attitude Change 11% [6,9,10] System/approach evaluation only (usability, acceptance, functionalities, perceived impact on behavioural change) 48.6% [7][8][9]11,18,19,[28][29][30][31]36,40,42,45] 1 Note that the sum of the percentages provided in the table does not add up to 100 as there are studies that report more than one evaluation targets (i.e., studies that report results on users' behavioral change as well as results on the evaluation of the system/approach used in the study).…”

Section: Evaluation Target % Of Total Studies 1 Studymentioning

confidence: 99%

“…Behavioural Change: [6,7,11,19,20,29,31,41] Attitude Change: [6] System/Approach: [9,31] Not successful 18%…”

Section: Successful 65%mentioning

confidence: 99%

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Persuasive Technologies for Sustainable Mobility: State of the Art and Emerging Trends

et al. 2018

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Abstract:In recent years, persuasive interventions for inducing sustainable mobility behaviours have become an active research field. This review paper systematically analyses existing approaches and prototype systems as well as field studies and describes and classifies the persuasive strategies used for changing behaviours in the domain of mobility and transport. We provide a review of 44 papers on persuasive technology for sustainable transportation aiming to (i) answer important questions regarding the effectiveness of persuasive technology for changing mobility behaviours, (ii) summarize and highlight trends in the technology design, research methods, strategies and theories, (iii) uncover limitations of existing approaches and applications, and (iv) suggest directions for future research.

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Section: Persuasive Systems For Sustainable Mobilitymentioning

confidence: 99%

Section: Evaluation Target % Of Total Studies 1 Studymentioning

confidence: 99%

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Persuasive Technologies for Sustainable Mobility: State of the Art and Emerging Trends

et al. 2018

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“…In recent years, incentive mechanisms used to incentivize users to select behaviors that are beneficial to the incentive providers have been widely studied and applied in many fields. Typical examples can be promoting sales [1,2], hiring workers [3,4,5], encouraging beneficial behaviors [6,7]. Such processes are computationally modeled as incentive allocation problem, where the goal is to incentivize users with effective incentives under a budget limitation, such that the number of users who take the behavior that the incentive provider expects is maximized.…”

Section: Introductionmentioning

confidence: 99%

GAC: A Deep Reinforcement Learning Model Toward User Incentivization in Unknown Social Networks

Wu¹,

Li²,

Bai³

2022

Preprint

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In recent years, providing incentives to human users for attracting their attention and engagement has been widely adopted in many applications. To effectively incentivize users, most incentive mechanisms determine incentive values based on users' individual attributes, such as preferences. These approaches could be ineffective when such information is unavailable. Meanwhile, due to the budget limitation, the number of users who can be incentivized is also restricted. In this light, we intend to utilize social influence among users to maximize the incentivization. By directly incentivizing influential users in the social network, their followers and friends could be indirectly incentivized with fewer incentives or no incentive. However, it is difficult to identify influential users beforehand in the social network, as the influence strength between each pair of users is typically unknown. In this work, we propose an end-to-end reinforcement learning-based framework, named Geometric Actor-Critic (GAC), to discover effective incentive allocation policies under limited budgets. More specifically, the proposed approach can extract information from a high-level network representation for learning effective incentive allocation policies. The proposed GAC only requires the topology of the social network and does not rely on any prior information about users' attributes. We use three real-world social network datasets to evaluate the performance of the proposed GAC. The experimental results demonstrate the effectiveness of the proposed approach.

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“…As aforementioned, engaging influential users can help affect more users' behaviors under a limited budget. Hence, strong influential users suppose to be provided with more incentives for keeping them involved, while weak influential users or non-influential users may receive less even no incentives [50]. Meanwhile, the overall status of users in the network needs to be considered to adjust incentives as well, since the marginal effect of allocating incentives would decline with the increasing number of users who take the behavior that the incentive provider expects [21].…”

Section: Introductionmentioning

confidence: 99%

Identifying Influential Users in Unknown Social Networks for Adaptive Incentive Allocation Under Budget Restriction

Li²,

Shen³

et al. 2021

Preprint

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In recent years, recommendation systems have been widely applied in many domains. These systems are impotent in affecting users to choose the behavior that the system expects. Meanwhile, providing incentives has been proven to be a more proactive way to affect users' behaviors. Due to the budget limitation, the number of users who can be incentivized is restricted. In this light, we intend to utilize social influence existing among users to enhance the effect of incentivization. Through incentivizing influential users directly, their followers or friends in the social network are possibly incentivized indirectly. However, in many real-world scenarios, the topological structure of the network is usually unknown, which makes identifying influential users difficult. To tackle the aforementioned challenges, in this paper, we propose a novel algorithm specifically for exploring influential users in unknown networks, which can estimate the influential relationships among users based on their historical behaviors and without knowing the topology of the network. Meanwhile, we design an adaptive incentive allocation approach that determines incentive values based on users' preferences and their influence ability. We evaluate the performance of the proposed approaches by conducting experiments on both synthetic and real-world datasets. The experimental results demonstrate the effectiveness of the proposed approaches.

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GreenCommute: An Influence-Aware Persuasive Recommendation Approach for Public-Friendly Commute Options

Cited by 15 publications

References 8 publications

Persuasive Technologies for Sustainable Mobility: State of the Art and Emerging Trends

Persuasive Technologies for Sustainable Mobility: State of the Art and Emerging Trends

GAC: A Deep Reinforcement Learning Model Toward User Incentivization in Unknown Social Networks

Identifying Influential Users in Unknown Social Networks for Adaptive Incentive Allocation Under Budget Restriction

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