Abstract
Micro-videos have become very popular recently. While using a micro-video app, the user experiences are strongly affected by the ranking of micro-videos. Moreover, the micro-video recommendation is often required to satisfy multiple business indicators. The existing models mainly utilize multi-modal features whose acquisition cost is too high for start-up companies. In the paper, we propose a multi-task ranking model MARS for recommending micro-videos. MARS aims at two tasks: finishing playing prediction and playback time prediction. For providing high accuracy in performing these two tasks, MARS adopts the multi-expert structure and mines historical statistical information besides interactions between users and micro-videos. Results of offline experiments and online A/B tests show that MARS can achieve good performances on two tasks. Further, MARS has been deployed in a real-world production environment, serving thousands of users.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China under Grant No. 62072450 and the 2021 joint project with MX Media.
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Song, J. et al. (2023). MARS: A Multi-task Ranking Model for Recommending Micro-videos. In: Li, B., Yue, L., Tao, C., Han, X., Calvanese, D., Amagasa, T. (eds) Web and Big Data. APWeb-WAIM 2022. Lecture Notes in Computer Science, vol 13423. Springer, Cham. https://doi.org/10.1007/978-3-031-25201-3_16
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DOI: https://doi.org/10.1007/978-3-031-25201-3_16
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