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Diversified ranking on large graphs: an optimization viewpoint

Published: 21 August 2011 Publication History

Abstract

Diversified ranking on graphs is a fundamental mining task and has a variety of high-impact applications. There are two important open questions here. The first challenge is the measure - how to quantify the goodness of a given top-k ranking list that captures both the relevance and the diversity? The second challenge lies in the algorithmic aspect - how to find an optimal, or near-optimal, top-k ranking list that maximizes the measure we defined in a scalable way? In this paper, we address these challenges from an optimization point of view. Firstly, we propose a goodness measure for a given top-k ranking list. The proposed goodness measure intuitively captures both (a) the relevance between each individual node in the ranking list and the query; and (b) the diversity among different nodes in the ranking list. Moreover, we propose a scalable algorithm (linear wrt the size of the graph) that generates a provably near-optimal solution. The experimental evaluations on real graphs demonstrate its effectiveness and efficiency.

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    cover image ACM Conferences
    KDD '11: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
    August 2011
    1446 pages
    ISBN:9781450308137
    DOI:10.1145/2020408
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    Publication History

    Published: 21 August 2011

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    Author Tags

    1. diversity
    2. graph mining
    3. ranking
    4. scalability

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    Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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    • (2022)Provable randomized rounding for minimum-similarity diversificationData Mining and Knowledge Discovery10.1007/s10618-021-00811-236:2(709-738)Online publication date: 4-Jan-2022
    • (2020)Serendipity-based Points-of-Interest NavigationACM Transactions on Internet Technology10.1145/339119720:4(1-32)Online publication date: 1-Oct-2020
    • (2020)Testing the impact of semantics and structure on recommendation accuracy and diversityProceedings of the 12th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining10.1109/ASONAM49781.2020.9381334(250-257)Online publication date: 7-Dec-2020
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    • (2019)Online Social Media Recommendation Over Streams2019 IEEE 35th International Conference on Data Engineering (ICDE)10.1109/ICDE.2019.00088(938-949)Online publication date: Apr-2019
    • (2019)Semi-supervised Classification-based Local Vertex Ranking via Dual Generative Adversarial Nets2019 IEEE International Conference on Big Data (Big Data)10.1109/BigData47090.2019.9005595(1267-1273)Online publication date: Dec-2019
    • (2019)Diversity in Machine LearningIEEE Access10.1109/ACCESS.2019.29176207(64323-64350)Online publication date: 2019
    • (2018)Fusing Diversity in Recommendations in Heterogeneous Information NetworksProceedings of the Eleventh ACM International Conference on Web Search and Data Mining10.1145/3159652.3159720(414-422)Online publication date: 2-Feb-2018
    • (2018)DivGroup: A Diversified Approach to Divide Collection of Patterns into Uniform Groups2018 24th International Conference on Pattern Recognition (ICPR)10.1109/ICPR.2018.8546203(964-969)Online publication date: Aug-2018
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