Proceedings Article10.1145/502512.502525
Mining the network value of customers
Pedro Domingos,Matthew Richardson +1 more
- 26 Aug 2001
- pp 57-66
TL;DR: It is proposed to model also the customer's network value: the expected profit from sales to other customers she may influence to buy, the customers those may influence, and so on recursively, taking advantage of the availability of large relevant databases.
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Abstract: One of the major applications of data mining is in helping companies determine which potential customers to market to. If the expected profit from a customer is greater than the cost of marketing to her, the marketing action for that customer is executed. So far, work in this area has considered only the intrinsic value of the customer (i.e, the expected profit from sales to her). We propose to model also the customer's network value: the expected profit from sales to other customers she may influence to buy, the customers those may influence, and so on recursively. Instead of viewing a market as a set of independent entities, we view it as a social network and model it as a Markov random field. We show the advantages of this approach using a social network mined from a collaborative filtering database. Marketing that exploits the network value of customers---also known as viral marketing---can be extremely effective, but is still a black art. Our work can be viewed as a step towards providing a more solid foundation for it, taking advantage of the availability of large relevant databases.
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Citations
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TL;DR: The experimental results on real-world datasets demonstrate that the proposed algorithms are able to achieve matching blocking effect to the greedy algorithm as the increase in the number of positive seeds and often better than other heuristic algorithms, whereas they are four orders of magnitude faster than the greedy algorithms.
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Identifying Influential Individuals on Large-Scale Social Networks: A Community Based Approach
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On non-progressive spread of influence through social networks
TL;DR: It is proved that while the MinPTS problem is NP-complete for a restricted family of graphs, it admits a constant-factor approximation algorithm for power-law graphs and the convergence properties of these algorithms are studied and it is shown that the non-progressive model converges in at most O(|E(G)|) steps.
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Efficient Topic-aware Influence Maximization Using Preprocessing.
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- 01 Mar 2014
TL;DR: In this paper, the authors focus on the topic-aware influence maximization task and study preprocessing methods for these topics to avoid redoing influence maximisation for each item from scratch.
24
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