Despite the encouraging achievements, the practical application of recommendation systems still faces two key issues. The first is how to better understand the multimodal real-time requests that are the more mainstream request behavior in industrial scenarios; the other is how to effectively capture users' dynamic needs that change with temporal and spatial conditions. The breakthroughs in text understanding and generation capabilities of Large Language Models (LLMs) have demonstrated their tremendous potential in precise recommendation systems, particularly through the enhancement of the understanding of user intent. To address these issues, we propose a novel Spatial-Temporal Multimodal LLM for generative recommendation. Specifically, on the basis of the behavior data constructed from Alipay, spatial-temporal knowledge-guided fine-tuning module is proposed to capture specific needs in user real-time requests. Furthermore, a preference discovery module is developed to learn user preferences in visual queries from multimodal request perspective. Meanwhile, a personalized recommendation module is designed to aggregate spatial-temporal knowledge and user preferences for generative recommendation. Experimental results on a real-world deployed generative recommendation task from the ‘Explore' scenario in Alipay have demonstrated the effectiveness of the proposed framework.

Offline user identification is a scenario that users use their bio-information like faces as identification in offline venues, which has been applied in many offline scenarios such as verification in banks, check-in in hotels and making a purchase in offline merchants. In such a scenario, designing an identification approach to do extremely accurate offline user identification is critical. Most scenarios use faces to identify users and previous algorithms are mainly based on visual features and computer-vision models. However, due to the large variations such as pose, illumination and occlusions in offline scenarios, it remains a challenging problem for existing computer-vision algorithms to get a satisfying accuracy in real-world scenarios. Furthermore, billion-scale candidate users also require high efficiency and high accuracy for the approach.

Storm is a popular real-time processing system. However, our earlier experiment shows that the fixed configuration of Storm would lead to either significant resource waste or limited processing throughput. In this demonstration, we present AdaStorm, a system to dynamically adjust the Storm configuration according to current data stream properties. AdaStorm is designed to minimize the resource usage while still ensuring the same or even better real-time response. We will demonstrate that AdaStorm can achieve resource efficiency as well as data rate tolerance, compared to Storm system with fixed configuration. Video: https://youtu.be/YFPBFNdMbXM.

Distributed data stream management systems (DDSMS) are usually composed of upper layer relational query systems (RQS) and lower layer stream processing systems (SPS). When users submit new queries to RQS, a query planner needs to be converted into a directed acyclic graph (DAG) consisting of tasks which are running on SPS. Based on different query requests and data stream properties, SPS need to configure different deployments strategies. However, how to dynamically predict deployment configurations of SPS to ensure the processing throughput and low resource usage is a great challenge. This article presents OrientStream, a framework for automating characterization deployment in DDSMS using incremental machine learning techniques. By introducing the data-level, query plan-level, operator-level, and cluster-level's four-level feature extraction mechanism, we first use the different query workloads as training sets to predict the resource usage by DDSMS, and select the optimal resource configuration from candidate settings based on the current query requests and stream properties, then migrate the operator state by introducing dynamic reconfiguration. Finally, we validate our approach on the open source SPS-Storm. In view of the application scenarios with long monitoring cycle and non-frequent data fluctuation, experiments show that OrientStream can reduce CPU usage of 8-15 percent and memory usage of 38-48 percent, respectively.

Distributed data stream management systems (DDSMS) are usually composed of upper layer relational query systems (RQS) and lower layer stream processing systems (SPS). When users submit new queries to RQS, a query planner needs to be converted into a directed acyclic graph (DAG) consisting of tasks which are running on SPS. Based on different query requests and data stream properties, SPS need to configure different deployments strategies. However, how to dynamically predict deployment configurations of SPS to ensure the processing throughput and low resource usage is a great challenge. This article presents OrientStream, a framework for dynamic resource allocation in DDSMS using incremental machine learning techniques. By introducing the data-level, query plan-level, operator-level and cluster-level's four-level feature extraction mechanism, we firstly use the different query workloads as training sets to predict the resource usage of DDSMS and then select the optimal resource configuration from candidate settings based on the current query requests and stream properties. Finally, we validate our approach on the open source SPS--Storm. Experiments show that OrientStream can reduce CPU usage of 8%-15% and memory usage of 38%-48% respectively.