Hiformer: Heterogeneous Feature Interactions Learning with Transformers for Recommender Systems

Learning feature interaction is the critical backbone to building recommender systems. In web-scale applications, learning feature interaction is extremely challenging due to the sparse and large input feature space; meanwhile, manually crafting effective feature interactions is infeasible because of the exponential solution space. We propose to leverage a Transformer-based architecture with attention layers to automatically capture feature interactions. Transformer architectures have witnessed great success in many domains, such as natural language processing and computer vision. However, there has not been much adoption of Transformer architecture for feature interaction modeling in industry. We aim at closing the gap. We identify two key challenges for applying the vanilla Transformer architecture to web-scale recommender systems: (1) Transformer architecture fails to capture the heterogeneous feature interactions in the self-attention layer; (2) The serving latency of Transformer architecture might be too high to be deployed in web-scale recommender systems. We first propose a heterogeneous self-attention layer, which is a simple yet effective modification to the self-attention layer in Transformer, to take into account the heterogeneity of feature interactions. We then introduce \textsc{Hiformer} (\textbf{H}eterogeneous \textbf{I}nteraction Trans\textbf{former}) to further improve the model expressiveness. With low-rank approximation and model pruning, \hiformer enjoys fast inference for online deployment. Extensive offline experiment results corroborates the effectiveness and efficiency of the \textsc{Hiformer} model. We have successfully deployed the \textsc{Hiformer} model to a real world large scale App ranking model at Google Play, with significant improvement in key engagement metrics (up to +2.66\%)

Effect of Airflow Field in the Tangential-Longitudinal Flow Threshing and Cleaning System on Harvesting Performance

The threshing and cleaning device in the grain combine harvester is located in the same airtight space, and the air flow field in it should also be studied and tested as a whole system. In order to study the distribution of air flow field and the influence of working parameters on the air flow field in the internal space of threshing and cleaning system, the method of predicting harvest performance indexes (grain loss rate and grain impurity rate) by air flow field analysis was explored. First of all, taking the longitudinal grain combine harvester of our research group as the test object and taking the rotating speed of centrifugal fan, the angle of fan plate, the opening of chaffer, and the rotating speed of threshing cylinder as the research factors, the internal space flow channel model of threshing and cleaning system under different working conditions was established and CFD software was used to simulate and analyze the air flow field. At the same time, the hot wire anemometer is used to measure and verify the distribution of air flow field in the threshing and cleaning system under various working conditions. Then, the harvest performance index of the threshing and cleaning system under the rated feeding rate is tested under the corresponding working conditions to find the relationship between the distribution of air flow field and harvest performance, put forward the corresponding analysis and prediction methods, and establish the mathematical relationship model between the simulated air flow field and harvest performance index. The results of simulation and experiment show that the average air velocity can more accurately reflect the cleaning performance. The mathematical function of the relation curve is Y = 11.71X − 4.76, and the prediction error is within 9.4%. The air velocity in the middle area of the vibrating screen is approximately in proportion to the cleaning performance, which provides the theoretical and experimental basis for the design of the threshing and cleaning device and the adjustment of the working parameters in the field harvest. In addition, it can save the design time and cost and reduce the seasonal impact of field experiment

Vibration Response of the Crawler Combine Chassis Frame Excited by the Engine

There are many problems of vibration and noise in combine working. As the main power source and excitation source of a combine, the working state of an engine directly affects the reliability and stability of the whole harvester. In order to analyze the vibration response characteristics of a chassis frame under engine excitation, the vibration mechanism and theoretical excitation characteristics of an engine vibration source on a crawler combine harvester are analyzed in this paper, and the vibration response of chassis under engine excitation is tested and analyzed. After theoretical derivation, a two-degree-of-freedom dynamic model of an engine and chassis is established. The experimental results show that the up and down vibration generated by the engine is the main vibration source in the Z direction, and the main excitation frequency is the second-order firing frequency. This paper provides a theoretical reference and experimental basis for vibration reduction and noise reduction of combine and vibration characteristics of the chassis frame