Enhancing Taobao Display Advertising with Multimodal Representations: Challenges, Approaches and Insights
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arXiv
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913450217701376 |
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| author | Sheng, Xiang-Rong Yang, Feifan Gong, Litong Wang, Biao Chan, Zhangming Zhang, Yujing Cheng, Yueyao Zhu, Yong-Nan Ge, Tiezheng Zhu, Han Jiang, Yuning Xu, Jian Zheng, Bo |
| author_facet | Sheng, Xiang-Rong Yang, Feifan Gong, Litong Wang, Biao Chan, Zhangming Zhang, Yujing Cheng, Yueyao Zhu, Yong-Nan Ge, Tiezheng Zhu, Han Jiang, Yuning Xu, Jian Zheng, Bo |
| contents | Despite the recognized potential of multimodal data to improve model accuracy, many large-scale industrial recommendation systems, including Taobao display advertising system, predominantly depend on sparse ID features in their models. In this work, we explore approaches to leverage multimodal data to enhance the recommendation accuracy. We start from identifying the key challenges in adopting multimodal data in a manner that is both effective and cost-efficient for industrial systems. To address these challenges, we introduce a two-phase framework, including: 1) the pre-training of multimodal representations to capture semantic similarity, and 2) the integration of these representations with existing ID-based models. Furthermore, we detail the architecture of our production system, which is designed to facilitate the deployment of multimodal representations. Since the integration of multimodal representations in mid-2023, we have observed significant performance improvements in Taobao display advertising system. We believe that the insights we have gathered will serve as a valuable resource for practitioners seeking to leverage multimodal data in their systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_19467 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enhancing Taobao Display Advertising with Multimodal Representations: Challenges, Approaches and Insights Sheng, Xiang-Rong Yang, Feifan Gong, Litong Wang, Biao Chan, Zhangming Zhang, Yujing Cheng, Yueyao Zhu, Yong-Nan Ge, Tiezheng Zhu, Han Jiang, Yuning Xu, Jian Zheng, Bo Information Retrieval Machine Learning Despite the recognized potential of multimodal data to improve model accuracy, many large-scale industrial recommendation systems, including Taobao display advertising system, predominantly depend on sparse ID features in their models. In this work, we explore approaches to leverage multimodal data to enhance the recommendation accuracy. We start from identifying the key challenges in adopting multimodal data in a manner that is both effective and cost-efficient for industrial systems. To address these challenges, we introduce a two-phase framework, including: 1) the pre-training of multimodal representations to capture semantic similarity, and 2) the integration of these representations with existing ID-based models. Furthermore, we detail the architecture of our production system, which is designed to facilitate the deployment of multimodal representations. Since the integration of multimodal representations in mid-2023, we have observed significant performance improvements in Taobao display advertising system. We believe that the insights we have gathered will serve as a valuable resource for practitioners seeking to leverage multimodal data in their systems. |
| title | Enhancing Taobao Display Advertising with Multimodal Representations: Challenges, Approaches and Insights |
| topic | Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2407.19467 |