Action is All You Need: Dual-Flow Generative Ranking Network for Recommendation

We introduce the Dual-Flow Generative Ranking Network (DFGR), a two-stream architecture designed for recommendation systems. DFGR integrates innovative interaction patterns between real and fake flows within the QKV modules of the self-attention mechanism, enhancing both training and inference efficiency. This approach effectively addresses a key limitation observed in Meta's proposed HSTU generative recommendation approach, where heterogeneous information volumes are mapped into identical vector spaces, leading to training instability. Unlike traditional recommendation models, DFGR only relies on user history behavior sequences and minimal attribute information, eliminating the need for extensive manual feature engineering. Comprehensive evaluations on open-source and industrial datasets reveal DFGR's superior performance compared to established baselines such as DIN, DCN, DIEN, and DeepFM. We also investigate optimal parameter allocation strategies under computational constraints, establishing DFGR as an efficient and effective next-generation generate ranking paradigm.

End-to-end training of Multimodal Model and ranking Model

Traditional recommender systems heavily rely on ID features, which often encounter challenges related to cold-start and generalization. Modeling pre-extracted content features can mitigate these issues, but is still a suboptimal solution due to the discrepancies between training tasks and model parameters. End-to-end training presents a promising solution for these problems, yet most of the existing works mainly focus on retrieval models, leaving the multimodal techniques under-utilized. In this paper, we propose an industrial multimodal recommendation framework named EM3: End-to-end training of Multimodal Model and ranking Model, which sufficiently utilizes multimodal information and allows personalized ranking tasks to directly train the core modules in the multimodal model to obtain more task-oriented content features, without overburdening resource consumption. First, we propose Fusion-Q-Former, which consists of transformers and a set of trainable queries, to fuse different modalities and generate fixed-length and robust multimodal embeddings. Second, in our sequential modeling for user content interest, we utilize Low-Rank Adaptation technique to alleviate the conflict between huge resource consumption and long sequence length. Third, we propose a novel Content-ID-Contrastive learning task to complement the advantages of content and ID by aligning them with each other, obtaining more task-oriented content embeddings and more generalized ID embeddings. In experiments, we implement EM3 on different ranking models in two scenario, achieving significant improvements in both offline evaluation and online A/B test, verifying the generalizability of our method. Ablation studies and visualization are also performed. Furthermore, we also conduct experiments on two public datasets to show that our proposed method outperforms the state-of-the-art methods.