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How to predict recommendation lists that users do not like

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  • Gu, Ke
  • Fan, Ying
  • Di, Zengru

Abstract

It is obviously that many user-object online rating systems usually contain the information of the users’ attitudes: like or dislike the objects, these systems can be represented by signed bipartite networks. The common recommendation systems work on unsigned networks. Even if some consider the negative edges, they are all concerned with the objects that the recommended user likes. However, the objects that the user does not like are more personalized. Based on Network-Based Inference (NBI) and signed bipartite networks, we proposed Signed Network-Based Inference (SNBI) to provide the negative recommendation list, which predicts the objects that users dislike. The SNBI algorithm includes two mechanisms: When allocating resources on signed bipartite networks, first method (SNBI-1) does not allocate resources while second method (SNBI-2) reduces resources if there is a negative edge. By comparing the results on the actual data sets with NBI, we found that SNBI-2 which takes into account the role of the negative edges can better predict the objects that user does not like while maintaining the validity of the positive recommendation list, then gives more personalized recommendation.

Suggested Citation

  • Gu, Ke & Fan, Ying & Di, Zengru, 2020. "How to predict recommendation lists that users do not like," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).
    • Handle: RePEc:eee:phsmap:v:537:y:2020:i:c:s0378437119315304
    DOI: 10.1016/j.physa.2019.122684
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    References listed on IDEAS

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    2. Agrawal, Smita & Patel, Atul, 2021. "SAG Cluster: An unsupervised graph clustering based on collaborative similarity for community detection in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 563(C).

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