The field of recommendation systems is moving towards more sophisticated and efficient models, with a focus on incorporating multi-modal information, lifelong sequential behavior, and generative approaches. Recent research has explored the use of diffusion models, multi-interest networks, and attention mechanisms to improve the accuracy and scalability of recommendation systems. Notably, the use of pre-trained large-scale foundation models has shown promising results in capturing users' general interests across multiple scenarios and tasks. Furthermore, the development of novel training strategies and frameworks, such as multi-attribution learning and discrete diffusion-based generative models, has led to significant improvements in click-through rate prediction and conversion rate prediction.

Some noteworthy papers in this area include: INFNet, which proposes a task-aware information flow network for large-scale recommendation systems, achieving state-of-the-art performance and significant gains in revenue and click-through rate. TBGRecall, which introduces a generative retrieval model for e-commerce recommendation scenarios, outperforming state-of-the-art methods and exhibiting a clear scaling law trend. AsymDiffRec, which proposes an asymmetric diffusion recommendation model, improving the effectiveness of generative recommendation systems for e-commerce applications. LIME, which presents a lifetime-aware interest matching framework for news recommendation, consistently outperforming state-of-the-art methods and improving recommendation accuracy. ENCODE, which proposes an efficient two-stage long-term sequence modeling approach, achieving a desirable balance between online service efficiency and precision. DGenCTR, which introduces a discrete diffusion-based generative CTR training framework, alleviating the constraints of traditional discriminative models in label-scarce space. MISS, which represents a pioneering exploration of leveraging multi-modal information and lifelong sequence modeling within the advanced tree-based retrieval model. LFM4Ads, which proposes an all-representation multi-granularity transfer framework for ads recommendation, achieving significant improvements in GMV lift and estimated annual revenue increases. MAL, which presents a novel multi-attribution learning framework for CVR prediction, integrating signals from multiple attribution perspectives to better capture underlying patterns driving user conversions. DiffuMIN, which proposes a diffusion-driven multi-interest network for CTR prediction, modeling long-term user behaviors and thoroughly exploring the user interest space. SUAN, which constructs a CTR model with accuracy scalable to the model grade and data size, and distills the knowledge implied in this model into its lightweight model for online deployment. LongRetriever, which presents a practical framework for incorporating ultra-long sequences into the retrieval stage of recommenders, enabling candidate-specific interaction between user sequence and candidate item.