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Analyzing Consumer-Product Graphs: Empirical Findings and Applications in Recommender Systems

Author

Listed:
  • Zan Huang

    (Department of Supply Chain and Information Systems, Pennsylvania State University, 419 Business Building, University Park, Pennsylvania 16802)

  • Daniel D. Zeng

    (Department of Management Information Systems, The University of Arizona, McClelland Hall 430, 1130 East Helen Street, Tucson, Arizona 85721)

  • Hsinchun Chen

    (Department of Management Information Systems, The University of Arizona, McClelland Hall 430, 1130 East Helen Street, Tucson, Arizona 85721)

Abstract
We apply random graph modeling methodology to analyze bipartite consumer-product graphs that represent sales transactions to better understand consumer purchase behavior in e-commerce settings. Based on two real-world e-commerce data sets, we found that such graphs demonstrate topological features that deviate significantly from theoretical predictions based on standard random graph models. In particular, we observed consistently larger-than-expected average path lengths and a greater-than-expected tendency to cluster. Such deviations suggest that the consumers' product choices are not random even with the consumer and product attributes hidden. Our findings provide justification for a large family of collaborative filtering-based recommendation algorithms that make product recommendations based only on previous sales transactions. By analyzing the simulated consumer-product graphs generated by models that embed two representative recommendation algorithms, we found that these recommendation algorithm-induced graphs generally provided a better match with the real-world consumer-product graphs than purely random graphs. However, consistent deviations in topological features remained. These findings motivated the development of a new recommendation algorithm based on graph partitioning, which aims to achieve high clustering coefficients similar to those observed in the real-world e-commerce data sets. We show empirically that this algorithm significantly outperforms representative collaborative filtering algorithms in situations where the observed clustering coefficients of the consumer-product graphs are sufficiently larger than can be accounted for by these standard algorithms.

Suggested Citation

  • Zan Huang & Daniel D. Zeng & Hsinchun Chen, 2007. "Analyzing Consumer-Product Graphs: Empirical Findings and Applications in Recommender Systems," Management Science, INFORMS, vol. 53(7), pages 1146-1164, July.
    • Handle: RePEc:inm:ormnsc:v:53:y:2007:i:7:p:1146-1164
    DOI: 10.1287/mnsc.1060.0619
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    References listed on IDEAS

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    6. Lingling Zhang & Jing Li & Qiuliu Zhang & Fan Meng & Weili Teng, 2019. "Domain Knowledge-Based Link Prediction in Customer-Product Bipartite Graph for Product Recommendation," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(01), pages 311-338, January.
    7. Zan Huang & Daniel Dajun Zeng, 2011. "Why Does Collaborative Filtering Work? Transaction-Based Recommendation Model Validation and Selection by Analyzing Bipartite Random Graphs," INFORMS Journal on Computing, INFORMS, vol. 23(1), pages 138-152, February.
    8. 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).
    9. Ali Cevahir, 0. "Index partitioning through a bipartite graph model for faster similarity search in recommendation systems," Information Systems Frontiers, Springer, vol. 0, pages 1-16.
    10. Daniel Zeng & Yong Liu & Ping Yan & Yanwu Yang, 2021. "Location-Aware Real-Time Recommender Systems for Brick-and-Mortar Retailers," INFORMS Journal on Computing, INFORMS, vol. 33(4), pages 1608-1623, October.
    11. Loredana MOCEAN & Ciprian Marcel POP, 2012. "Marketing Recommender Systems: A New Approach in Digital Economy," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 16(4), pages 142-149.
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    13. Chen, Zhen-Yu & Fan, Zhi-Ping & Sun, Minghe, 2021. "Tensorial graph learning for link prediction in generalized heterogeneous networks," European Journal of Operational Research, Elsevier, vol. 290(1), pages 219-234.
    14. Yuanchun Jiang & Jennifer Shang & Chris F. Kemerer & Yezheng Liu, 2011. "Optimizing E-tailer Profits and Customer Savings: Pricing Multistage Customized Online Bundles," Marketing Science, INFORMS, vol. 30(4), pages 737-752, July.
    15. Ali Cevahir, 2017. "Index partitioning through a bipartite graph model for faster similarity search in recommendation systems," Information Systems Frontiers, Springer, vol. 19(5), pages 1161-1176, October.
    16. Shi, Xiaoyu & Shang, Ming-Sheng & Luo, Xin & Khushnood, Abbas & Li, Jian, 2017. "Long-term effects of user preference-oriented recommendation method on the evolution of online system," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 467(C), pages 490-498.
    17. Yicheng Song & Nachiketa Sahoo & Elie Ofek, 2019. "When and How to Diversify—A Multicategory Utility Model for Personalized Content Recommendation," Management Science, INFORMS, vol. 65(8), pages 3737-3757, August.
    18. Christian Matt & Thomas Hess, 2016. "Product fit uncertainty and its effects on vendor choice: an experimental study," Electronic Markets, Springer;IIM University of St. Gallen, vol. 26(1), pages 83-93, February.
    19. Li, Sheng-Nan & Guo, Qiang & Yang, Kai & Liu, Jian-Guo & Zhang, Yi-Cheng, 2018. "Uncovering the popularity mechanisms for Facebook applications," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 494(C), pages 422-429.
    20. S.G. Li & L. Shi, 2014. "The recommender system for virtual items in MMORPGs based on a novel collaborative filtering approach," International Journal of Systems Science, Taylor & Francis Journals, vol. 45(10), pages 2100-2115, October.
    21. Wu, Yujia & Lan, Wei & Fan, Xinyan & Fang, Kuangnan, 2024. "Bipartite network influence analysis of a two-mode network," Journal of Econometrics, Elsevier, vol. 239(2).
    22. Yin, Chun-Xia & Peng, Qin-Ke & Chu, Tao, 2012. "Personal artist recommendation via a listening and trust preference network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(5), pages 1991-1999.

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