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Time-aware conversion prediction

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Abstract

The importance of product recommendation has been well recognized as a central task in business intelligence for e-commerce websites. Interestingly, what has been less aware of is the fact that different products take different time periods for conversion. The “conversion” here refers to actually a more general set of pre-defined actions, including for example purchases or registrations in recommendation and advertising systems. The mismatch between the product’s actual conversion period and the application’s target conversion period has been the subtle culprit compromising many existing recommendation algorithms.

The challenging question: what products should be recommended for a given time period to maximize conversion—is what has motivated us in this paper to propose a rank-based time-aware conversion prediction model (rTCP), which considers both recommendation relevance and conversion time. We adopt lifetime models in survival analysis to model the conversion time and personalize the temporal prediction by incorporating context information such as user preference. A novel mixture lifetime model is proposed to further accommodate the complexity of conversion intervals. Experimental results on two real-world data sets illustrate the high goodness of fit of our proposed model rTCP and demonstrate its effectiveness in time-aware conversion rate prediction for advertising and product recommendation.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (NSFC) (Grant Nos. 61472141, 61532021 and 61021004), Shanghai Knowledge Service Platform Project (ZF1213) and Shanghai Leading Academic Discipline Project (B412).

Author information

Authors and Affiliations

  1. Shanghai Key Laboratory of Trustworthy Computing, Institute for Data Science and Engineering, East China Normal University, Shanghai, 200062, China

    Wendi Ji & Xiaoling Wang

  2. School of Information Systems, Singapore Management University, Singapore, 188065, Singapore

    Feida Zhu

Authors
  1. Wendi Ji
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  2. Xiaoling Wang
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  3. Feida Zhu
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Corresponding author

Correspondence to Xiaoling Wang.

Additional information

Wendi Ji is currently working toward the doctoral degree in the Software Engineering Institute at East China Normal University, China. Her research interests mainly include user behavior analysis and data mining.

Xiaoling Wang received the bachelor master and doctoral degrees from Southeastern University, China in 1997, 2000, and 2003, respectively. She is currently a professor, and the vice dean in Software Engineering Institute, East China Normal University, China. Her research interests mainlyinclude Web data management, data service technology and applications.

Feida Zhu is an assistant professor at the School of Information Systems of Singapore Management University (SMU), Singapore. He obtained his PhD in Computer Science from the University of Illinois at Urbana-Champaign (UIUC), USA in 2009 and his BS in computer science from Fudan University, China in 2001. His current research interests include large scale data mining, graph/network mining, and social network analysis.

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Ji, W., Wang, X. & Zhu, F. Time-aware conversion prediction. Front. Comput. Sci. 11, 702–716 (2017). https://doi.org/10.1007/s11704-016-5546-y

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  • DOI: https://doi.org/10.1007/s11704-016-5546-y

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