Revenue Maximization on the Multi-grade Product

Teng, Ya Wen; Tai, Chih Hua; Yu, Philip S.; Chen, Ming-Syan

doi:10.1137/1.9781611975321.65

Cited by 17 publications

(14 citation statements)

References 6 publications

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“…The influence is assumed to decay along the path, and it is less likely to reach the nodes that are farther away from the root. Some studies investigate the IM for selecting seeds at different rounds [5], making exclusive adoption among items [17], [18], avoiding spamming seeds by overwhelming promotions [31], learning diffusion probabilities of different items [9], and maximizing utility-based adoption among desired items [32]. However, they do not consider multiple promotions to promote a sequence of relevant items modeled by KG and meta-graphs.…”

Section: Related Workmentioning

confidence: 99%

“…In this paper, we formulate a new problem, named Influence Maximization based on Dynamic Personal Perception (referred to as IMDPP). In contrast to most previous works [16], [17] focusing on one item, given the social network, KG, and meta-graphs for different item relationships, IMDPP targets on multiple promotions to maximize the overall spread of influence by choosing items and selecting seed users for promotion at proper timings under a total budget, where users have different costs as seeds [3], [18], and each promotion allows multiple items to be promoted. We exploit personal item networks to capture dynamic personal perceptions of complementary and substitutable relationships between items.…”

Section: Introductionmentioning

confidence: 99%

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Influence Maximization Based on Dynamic Personal Perception in Knowledge Graph

Teng

Shi

Tai

et al. 2021

2021 IEEE 37th International Conference on Data Engineering (ICDE)

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Viral marketing on social networks, also known as Influence Maximization (IM), aims to select k users for the promotion of a target item by maximizing the total spread of their influence. However, most previous works on IM do not explore the dynamic user perception of promoted items in the process. In this paper, by exploiting the knowledge graph (KG) to capture dynamic user perception, we formulate the problem of Influence Maximization based on Dynamic Personal Perception (IMDPP) that considers user preferences and social influence reflecting the impact of relevant item adoptions. We prove the hardness of IMDPP and design an approximation algorithm, named Dynamic perception for seeding in target markets (Dysim), by exploring the concepts of dynamic reachability, target markets, and substantial influence to select and promote a sequence of relevant items. We evaluate the performance of Dysim in comparison with the stateof-the-art approaches using real social networks with real KGs. The experimental results show that Dysim effectively achieves up to 6.7 times of influence spread in large datasets over the state-of-the-art approaches.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Influence Maximization Based on Dynamic Personal Perception in Knowledge Graph

Teng

Shi

Tai

et al. 2021

2021 IEEE 37th International Conference on Data Engineering (ICDE)

Self Cite

View full text Add to dashboard Cite

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“…Whenever a spider SP i moves to a new position, it generates a vibration at its current position. The vibration Vib SP j of a spider SP j is perceived by another spider SP i and can be modeled in Equation (6).…”

Section: Proposed Workmentioning

confidence: 99%

“…Before moving a new position, every spider SP i calculates their fitness value and generates vibration using Equation ( 10) and Equation ( 7), respectively. Each spider SP i receives vibration from spider SP j with intensity expressed in Equation (6). The spider SP i selects the spider SP j having strongest intensity vibration and compares it with Vib tar .…”

Section: Proposed Workmentioning

confidence: 99%

“…Social networks propagate information about products via links between users using word‐of‐mouth effect. This word‐of‐mouth advertising has immense application potential in viral marketing, rumor control, social recommendation, revenue maximization, and network monitoring, etc. In viral marketing, the advertising companies give the free sample of the product to a group of user to maximize the product adoption using the word‐of‐mouth effect.…”

Section: Introductionmentioning

confidence: 99%

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IM‐SSO: Maximizing influence in social networks using social spider optimization

Singh

Kumar

Singh

2019

Concurrency and Computation

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Summary Online social networks play a pivotal role in the propagation of information and influence as in the form of word‐of‐mouth spreading. The influence maximization (IM) problem is a fundamental problem to identify a small set of individuals, which have a maximal influence spread in the social network. Unfortunately, the IM problem is NP‐hard. It has been depicted that a hill‐climbing greedy approach gives a good approximation guarantee. However, it is inefficient to run on large‐scale social networks. In this paper, a global influence evaluation function is presented for the IM optimization problem. The global influence evaluation function provides a reliable expected diffusion value of influence spread under the traditional diffusion models. To optimize global influence evaluation function, an influence maximization algorithm based on social spider optimization (IM‐SSO) is presented. IM‐SSO redefines the representation and update rule of spider's vibration and performs random walk towards target vibration. The algorithm uses a jump away process to overcome the weakness of premature convergence. The experimental results on six real‐world social networks show that the proposed algorithm is more effective than the state‐of‐the‐art heuristics and more time‐efficient than CELF++, static greedy, and PSO with an approximate influence spread.

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