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Link prediction based on a spatial distribution model with fuzzy link importance

Author

Listed:
  • Ai, Jun
  • Su, Zhan
  • Li, Yan
  • Wu, Chunxue

Abstract

Most of link prediction methods in recommender systems focuse on selecting neighbors according to similarities between objects. And the prediction of ratings is calculated based on the selected neighbors’ ratings. Thus, a novel model of link prediction is proposed in this paper. With the construction of an object network, neighbor selection can be obtained by the distance between two different objects, which are given positions in a spatial distribution topology by algorithm. A fuzzy link importance measure is also presented to balance the topology density and spatial distribution of the network. We use number of tags shared by objects, as well as ratings on objects given by different users, to measure the weight between each pair of nodes. Moreover, the method based on shared tags avoids cold start for new objects. And the algorithm considering rating information improves the accuracy of prediction compared to some existing results.

Suggested Citation

  • Ai, Jun & Su, Zhan & Li, Yan & Wu, Chunxue, 2019. "Link prediction based on a spatial distribution model with fuzzy link importance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 527(C).
    • Handle: RePEc:eee:phsmap:v:527:y:2019:i:c:s0378437119306995
    DOI: 10.1016/j.physa.2019.121155
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    Citations

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    Cited by:

    1. Su, Zhan & Zheng, Xiliang & Ai, Jun & Shen, Yuming & Zhang, Xuanxiong, 2020. "Link prediction in recommender systems based on vector similarity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 560(C).
    2. Ai, Jun & Cai, Yifang & Su, Zhan & Zhang, Kuan & Peng, Dunlu & Chen, Qingkui, 2022. "Predicting user-item links in recommender systems based on similarity-network resource allocation," Chaos, Solitons & Fractals, Elsevier, vol. 158(C).

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