Tencent Advertising Algorithm Challenge 2025: All-Modality Generative Recommendation

TL;DR

This paper introduces the Tencent Advertising Algorithm Challenge 2025, providing large-scale, multi-modal datasets for generative recommendation in advertising, along with baseline models and evaluation protocols to foster research in this emerging field.

Contribution

It presents new all-modality datasets derived from real Tencent Ads logs, defines the task for generative recommendation, and offers baseline models and evaluation methods for industrial-scale research.

Findings

Datasets include TencentGR-1M and TencentGR-10M with rich multi-modal data.

Baseline models demonstrate the feasibility of generative recommendation in advertising.

Weighted evaluation emphasizes high-value conversion events.

Abstract

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.