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Improved collaborative filtering with intensity-based contraction

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Abstract

Recommendation systems are essential tools for piquing consumers’ interests and stimulating consumption in today’s electronic commerce, and the quality of these systems depends on the employed filtering algorithms. Therefore, improving the performance of these algorithms is an important issue. In this paper, we design an intensity-based contraction (IC) algorithm that works in combination with other machine-learning algorithms in model-based collaborative filtering, which is currently the most popular filtering algorithm. The main challenges for this algorithm are sparseness of the database and lack of scalability. To demonstrate how IC is used, we implemented IC clustering as an example, which can effectively reduce the sparseness of the database and improve the efficiency. Moreover, we created a scalable IC on a MapReduce model, the scalability of which is demonstrated with actual experiments.

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Acknowledgments

This work was supported by National Natural Science Foundation of China (No.61170268).

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Author notes

    Authors and Affiliations

    1. School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China

      Baojiang Cui, Haifeng Jin & Jiangdong Deng

    2. National Engineering Laboratory for Mobile Network Security, Beijing, China

      Baojiang Cui, Haifeng Jin & Jiangdong Deng

    3. Department of Computer and Information Security, College of Information Technical Science, Nankai University, Tianjin, China

      Zheli Liu

    4. Key Laboratory of Network Security and Cryptology, Fujian Normal University, Fuzhou, China

      Zheli Liu

    Authors
    1. Baojiang Cui
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    2. Haifeng Jin
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    3. Zheli Liu
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    4. Jiangdong Deng
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    Corresponding author

    Correspondence to Baojiang Cui.

    Additional information

    Z. Liu and B. Cui contribute to this work equally.

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    Cui, B., Jin, H., Liu, Z. et al. Improved collaborative filtering with intensity-based contraction. J Ambient Intell Human Comput 6, 661–674 (2015). https://doi.org/10.1007/s12652-015-0284-9

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