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Maximizing the influence of bichromatic reverse k nearest neighbors in geo-social networks

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Abstract

Geo-social networks offer opportunities for the marketing and promotion of geo-located services. In this setting, we explore a new problem, called Maximizing the Influence of Bichromatic Reverse k Nearest Neighbors (MaxInfBRkNN). The objective is to find a set of points of interest (POIs), which are geo-textually and socially relevant to social influencers who are expected to largely promote the POIs online. In other words, the problem aims to detect an optimal set of POIs with the largest word-of-mouth (WOM) marketing potential. This functionality is useful in various real-life applications, including social advertising, location-based viral marketing, and personalized POI recommendation. However, solving MaxInfBRkNN with theoretical guarantees is challenging because of the prohibitive overheads on BRkNN retrieval in geo-social networks, and the NP and #P-hardness of finding the optimal POI set. To achieve practical solutions, we present a framework with carefully designed indexes, efficient batch BRkNN processing algorithms, and alternative POI selection policies that support both approximate and heuristic solutions. Extensive experiments on real and synthetic datasets demonstrate the good performance of our proposed methods.

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Data availability

The codes and corresponding datasets are available at https://github.com/ZJU-DAILY/MaxInfBRkNN.

Notes

  1. https://edition.cnn.com/business/newsfeeds/globenewswire/7812666.html

  2. The Zipfian distribution indicates that most of keywords in the datasets appear infrequently [1]

  3. http://www.yelp.com/dataset_challenge/

  4. https://www.openstreetmap.org/

  5. http://www.dis.uniroma1.it/challenge9/

  6. Zipf’s law predicts that the frequency ft of a keyword t in real-world datasets satisfies \(f_{t} \propto \frac {1}{r_{t}^{\alpha }}\), where rt is the rank of t and α ≈ 1, which means most keywords in the datasets occur infrequently [1].

  7. As POIs often have multiple keywords, infrequent keywords also can be covered by these POIs.

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Acknowledgements

This work was supported in part by the NSFC under Grants No. (62025206, 61972338, and 62102351). Yunjun Gao is the corresponding author of this work.

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Authors and Affiliations

  1. College of Computer Science, Zhejiang University, Hangzhou, 310027, China

    Pengfei Jin, Lu Chen, Yunjun Gao, Xueqin Chang & Zhanyu Liu

  2. Department of Kunpeng Computing Product, Huawei Technologies CO., LTD, Shenzhen, 518129, China

    Shu Shen

  3. Department of Computer Science, Aalborg University, Aalborg, 410000, Denmark

    Christian S. Jensen

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  1. Pengfei Jin
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Contributions

Pengfei Jin gave the main idea of this paper, programmed the codes and made experiments. The first draft of the manuscript was written by Pengfei Jin and Lu Chen. The material preparation includes figures, data analysis were performed by Pengfei Jin, Xueqin Chang and Zhanyu Liu. Yunjun Gao and Christian S. Jensen gave suggestions and help to improve the manuscript. All the authors read and approved the final manuscript of this paper.

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Jin, P., Chen, L., Gao, Y. et al. Maximizing the influence of bichromatic reverse k nearest neighbors in geo-social networks. World Wide Web 26, 1567–1598 (2023). https://doi.org/10.1007/s11280-022-01096-1

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