Scaling-law has guided the language model design for past years, e.g., GPTs, enabling the estimation of expected model performance with respect to the size of learnable parameters and the scale of training samples. It is worth noting that the scaling laws of NLP cannot be directly applied to recommendation systems due to the following reasons: (1) The amount of training samples and model parameters is typically not the bottleneck for the model. Our recommendation system can generate over 50 billion user samples daily, and such a massive amount of training data can easily allow our model parameters to exceed 200 billion, surpassing many LLMs (about 100B). (2) It is essential to control FLOPs carefully in recommendation system. In training, we need to process a vast number of recommendation samples every day. During online inference, we must respond within milliseconds (LLMs usually take a few seconds). Considering the above differences with LLM, we can conclude that: for a RecSys model, compared to model parameters, the FLOPs is a more expensive factor that requires careful control.

In order to predict wind power quickly and accurately and reduce the negative impact of wind power instability on the grid, this study proposes an ultra-short-term wind power prediction model based on financial technical indicators and parameter optimization algorithms. Firstly, historical wind power time series data calculates the financial technical indicators. Secondly, the Monte Carlo method and rank ant colony algorithm are used to optimize the parameters of financial technical indicators calculation. Finally, the future wind power is predicted based on the XGBoost algorithm combining financial technical indicators with historical power. The proposed model is validated on several wind power datasets from different countries and compared with various comparative models, leading to several important conclusions: (1) Fintech indicators can effectively indicate the intrinsic characteristics of wind power time-series data. (2) The variational ant colony algorithm can make the financial technical indicators better fit the wind power time-series data trends. (3) The proposed model has high prediction accuracy and speed and has similar prediction accuracy to the mainstream deep learning models. (4) The proposed model is not limited by meteorological, geographical, and seasonal factors and can make predictions by relying only on historical wind power data.

Stock trend prediction is a hot issue in theFintechfield. Effective stock profiling is challenging due to highly non-stationary dynamics and complex interplays. Existing methods usually regard each stock independently or detect simplistic homogeneous structures. Practically, stock correlation originates from diverse aspects and underlying relationship signals are implicit in comprehensive graphs. Besides, RNNs are extensively used to simulate stock volatility while inadequate in capturing fine-granular patterns across local time snippets. To this end, in this paper we propose HATR-I, a Hierarchical Adaptive Temporal-Relational Interaction model to characterize and predict stock evolutions. Specifically, we grasp short- and long-term transition regularities of stock dynamics based on cascaded dilated convolutions and gating paths. By formulating different views of domain adjacency graphs into a unified multiplex network with edge attributes, we inject node- and semantic-level dual attention to refine the propagation of inter-stock collaborative information. Particularly, the stock pair matching is proceeding along each time-stage rather than until final compressed representations, meanwhile significant feature points and scales are identified considering the effect of time attenuation. Finally, we deduce latent shared clusters as global regularization to optimize the stock representations. Experiments on three real-world stock market datasets demonstrate the effectiveness of our proposed model.

Stock recommendation plays a critical role in modern quantitative trading. The large volumes of social media information such as investment reviews that delegate emotion-driven factors, together with price technical indicators formulate a “snapshot” of the evolving stock market profile. However, previous studies usually model the temporal trajectories of price and media modalities separately while losing their interrelated influences. Moreover, they mainly extract review semantics via sequential or attentive models, whereas the rich text associated knowledge is largely neglected. In this paper, we propose a novel heterogeneous interactive snapshot network for stock profiling and recommendation. We model investment reviews in each snapshot as a heterogeneous document graph, and develop a flexible hierarchical attentive propagation framework to capture fine-grained proximity features. Further, to learn stock embedding for ranking, we introduce a novel twins-GRU method, which tightly couples the media and price parallel sequences in a cross-interactive fashion to catch dynamic dependencies between successive snapshots. Our approach excels state-of-the-arts over 7.6% in terms of cumulative and risk-adjusted returns in trading simulations on both English and Chinese benchmarks.

Stock investment selection is a hard issue in the Fintech field due to non-stationary dynamics and complex market interdependencies. Existing studies are mostly based on RNNs, which struggle to capture interactive information among fine granular volatility patterns. Besides, they either treat stocks as isolated, or presuppose a fixed graph structure heavily relying on prior domain knowledge. In this paper, we propose a novel Adaptive Long-Short Pattern Transformer (ALSP-TF) for stock ranking in terms of expected returns. Specifically, we overcome the limitations of canonical self-attention including context and position agnostic, with two additional capacities: (i) fine-grained pattern distiller to contextualize queries and keys based on localized feature scales, and (ii) time-adaptive modulator to let the dependency modeling among pattern pairs sensitive to different time intervals. Attention heads in stacked layers gradually harvest short- and long-term transition traits, spontaneously boosting the diversity of representations. Moreover, we devise a graph self-supervised regularization, which helps automatically assimilate the collective synergy of stocks and improve the generalization ability of overall model. Experiments on three exchange market datasets show ALSP-TF’s superiority over state-of-the-art stock forecast methods.