Tencent Advertising Algorithm Challenge 2025: All-Modality Generative Recommendation
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| Natura: | Preprint |
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2026
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| author | Pan, Junwei Xue, Wei Zhou, Chao Zhou, Xing Fan, Lunan Wang, Yanbo Xin, Haoran Hu, Zhiyu Wang, Yaozheng Xu, Fengye Yang, Yurong Li, Xiaotian Huo, Junbang Ning, Wentao Sun, Yuliang Yin, Chengguo Zhang, Jun Huang, Shudong Xiao, Lei Yu, Huan King, Irwin Gu, Haijie Jiang, Jie |
| author_facet | Pan, Junwei Xue, Wei Zhou, Chao Zhou, Xing Fan, Lunan Wang, Yanbo Xin, Haoran Hu, Zhiyu Wang, Yaozheng Xu, Fengye Yang, Yurong Li, Xiaotian Huo, Junbang Ning, Wentao Sun, Yuliang Yin, Chengguo Zhang, Jun Huang, Shudong Xiao, Lei Yu, Huan King, Irwin Gu, Haijie Jiang, Jie |
| contents | Generative recommender systems are rapidly emerging as a new paradigm for recommendation, where collaborative identifiers and/or multi-modal content are mapped into discrete token spaces and user behavior is modelled with autoregressive sequence models. Despite progress on multi-modal recommendation datasets, there is still a lack of public benchmarks that jointly offer large-scale, realistic and fully all-modality data designed specifically for generative recommendation (GR) in industrial advertising. To foster research in this direction, we organised the Tencent Advertising Algorithm Challenge 2025, a global competition built on top of two all-modality datasets for GR: TencentGR-1M and TencentGR-10M. Both datasets are constructed from real de-identified Tencent Ads logs and contain rich collaborative IDs and multi-modal representations extracted with state-of-the-art embedding models. The preliminary track (TencentGR-1M) provides 1 million user sequences with up to 100 interacted items each, where each interaction is labeled with exposure and click signals, while the final track (TencentGR-10M) scales this to 10 million users and explicitly distinguishes between click and conversion events at both the sequence and target level. This paper presents the task definition, data construction process, feature schema, baseline GR model, evaluation protocol, and key findings from top-ranked and award-winning solutions. Our datasets focus on multi-modal sequence generation in an advertising setting and introduce weighted evaluation for high-value conversion events. We release our datasets at https://huggingface.co/datasets/TAAC2025 and baseline implementations at https://github.com/TencentAdvertisingAlgorithmCompetition/baseline_2025 to enable future research on all-modality generative recommendation at an industrial scale. The official website is https://algo.qq.com/2025. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_04976 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Tencent Advertising Algorithm Challenge 2025: All-Modality Generative Recommendation Pan, Junwei Xue, Wei Zhou, Chao Zhou, Xing Fan, Lunan Wang, Yanbo Xin, Haoran Hu, Zhiyu Wang, Yaozheng Xu, Fengye Yang, Yurong Li, Xiaotian Huo, Junbang Ning, Wentao Sun, Yuliang Yin, Chengguo Zhang, Jun Huang, Shudong Xiao, Lei Yu, Huan King, Irwin Gu, Haijie Jiang, Jie Information Retrieval Generative recommender systems are rapidly emerging as a new paradigm for recommendation, where collaborative identifiers and/or multi-modal content are mapped into discrete token spaces and user behavior is modelled with autoregressive sequence models. Despite progress on multi-modal recommendation datasets, there is still a lack of public benchmarks that jointly offer large-scale, realistic and fully all-modality data designed specifically for generative recommendation (GR) in industrial advertising. To foster research in this direction, we organised the Tencent Advertising Algorithm Challenge 2025, a global competition built on top of two all-modality datasets for GR: TencentGR-1M and TencentGR-10M. Both datasets are constructed from real de-identified Tencent Ads logs and contain rich collaborative IDs and multi-modal representations extracted with state-of-the-art embedding models. The preliminary track (TencentGR-1M) provides 1 million user sequences with up to 100 interacted items each, where each interaction is labeled with exposure and click signals, while the final track (TencentGR-10M) scales this to 10 million users and explicitly distinguishes between click and conversion events at both the sequence and target level. This paper presents the task definition, data construction process, feature schema, baseline GR model, evaluation protocol, and key findings from top-ranked and award-winning solutions. Our datasets focus on multi-modal sequence generation in an advertising setting and introduce weighted evaluation for high-value conversion events. We release our datasets at https://huggingface.co/datasets/TAAC2025 and baseline implementations at https://github.com/TencentAdvertisingAlgorithmCompetition/baseline_2025 to enable future research on all-modality generative recommendation at an industrial scale. The official website is https://algo.qq.com/2025. |
| title | Tencent Advertising Algorithm Challenge 2025: All-Modality Generative Recommendation |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2604.04976 |