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- W4313349634 abstract "The purpose of Profit Maximization (PM) problem in social advertising is gaining profit generated by adopters as much as possible by launching some initial adopters as information sources to trigger the spread of promotion information. Many related studies mainly focus on pure network, single product and one-dimension diffusion model. Inspired by real advertising activities in social networks, we propose the Dual-Attribute Compete (DAC) model where the information about competitive entities can spread simultaneously. Moreover, it not only captures attributes of both potential consumers and products but also reflects the relationship between them. Under DAC model, we study the Competition-based Generalized Self-profit Maximization (CGSM) problem aiming to select at most k individuals to form an optimal seed set as the source of information diffusion, so as to maximize the profit obtained by marketers. Considering that the objective function of CGSM problem is generally nonsubmodualr, we design R-CGSM algorithm. Based on martingale analysis and the concept of shapley value, it uses sandwich method to get a pretty good solution of CGSM problem. We evaluate our proposed algorithm by conducting experiments on one synthetic data set and three real world data sets. Results of experiments validate the effectiveness and accuracy of R-CGSM algorithm." @default.
- W4313349634 created "2023-01-06" @default.
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- W4313349634 date "2022-01-01" @default.
- W4313349634 modified "2023-10-16" @default.
- W4313349634 title "Competition-Based Generalized Self-profit Maximization in Dual-Attribute Networks" @default.
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- W4313349634 doi "https://doi.org/10.1007/978-3-031-20350-3_11" @default.
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