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Mining consuming Behaviors with Temporal Evolution for Personalized Recommendation in Mobile Marketing Apps

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Abstract

Recently, more and more mobile apps are employed in the marketing field with technical advances. Mobile marketing apps have become a prevalent way for enterprise marketing. Therefore, it has been an important and urgent problem to provide personalized and accurate recommendation in mobile marketing, with a large number of items and limited capability of mobile devices. Recommendation have been investigated widely, however, most existing approaches fail to consider the stability or change of users’ behaviors over time. In this paper, we first propose to mine the periodic trends of users’ consuming behavior from historical records by KNN(K-nearest neighbor) and SVR (support vector regression) based time series prediction, and predict the next time when a user re-purchases the item, so that we can recommend the items which users have purchased before at proper time. Second, we aim to find the regularity of users’ purchasing behavior during different life stages and recommend the new items that are needed and proper for their current life stage. In order to solve this, we mine the mapping model from items to user’s life stage first. Based on the model, users’ current life stage can be estimated from their recent behaviors. Finally, users will be recommended with new items which are proper to their estimated life stage. Experimental results show that it has improved the effectiveness of recommendation obviously by mining users’ consuming behaviors with temporal evolution.

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Acknowledgements

This work has been supported by the National Key R&D Program of China under grant 2018YFB1003800, National Natural Science Foundation of China (No.61772560), Natural Science Foundation of Hunan Province (No. 2019JJ40388).

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Authors and Affiliations

  1. Computing Center, Shanghai University, Shanghai, China

    Honghao Gao

  2. School of Computer Science and Engineering, Central South University, Changsha, China

    Li Kuang, Bin Guo & Kai Dou

  3. School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China

    Yuyu Yin

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  1. Honghao Gao
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  2. Li Kuang
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  3. Yuyu Yin
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  4. Bin Guo
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  5. Kai Dou
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Correspondence to Li Kuang.

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Gao, H., Kuang, L., Yin, Y. et al. Mining consuming Behaviors with Temporal Evolution for Personalized Recommendation in Mobile Marketing Apps. Mobile Netw Appl 25, 1233–1248 (2020). https://doi.org/10.1007/s11036-020-01535-1

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