COUPA: An Industrial Recommender System for Online to Offline Service Platforms

Aiming at helping users locally discovery retail services (e.g., entertainment and dinning), Online to Offline (O2O) service platforms have become popular in recent years, which greatly challenge current recommender systems. With the real data in Alipay, a feeds-like scenario for O2O services, we find that recurrence based temporal patterns and position biases commonly exist in our scenarios, which seriously threaten the recommendation effectiveness. To this end, we propose COUPA, an industrial system targeting for characterizing user preference with following two considerations: (1) Time aware preference: we employ the continuous time aware point process equipped with an attention mechanism to fully capture temporal patterns for recommendation. (2) Position aware preference: a position selector component equipped with a position personalization module is elaborately designed to mitigate position bias in a personalized manner. Finally, we carefully implement and deploy COUPA on Alipay with a cooperation of edge, streaming and batch computing, as well as a two-stage online serving mode, to support several popular recommendation scenarios. We conduct extensive experiments to demonstrate that COUPA consistently achieves superior performance and has potential to provide intuitive evidences for recommendation

Mirror-Yolo: An attention-based instance segmentation and detection model for mirrors

Mirrors can degrade the performance of computer vision models, however to accurately detect mirrors in images remains challenging. YOLOv4 achieves phenomenal results both in object detection accuracy and speed, nevertheless the model often fails in detecting mirrors. In this paper, a novel mirror detection method `Mirror-YOLO' is proposed, which mainly targets on mirror detection. Based on YOLOv4, the proposed model embeds an attention mechanism for better feature acquisition, and a hypercolumn-stairstep approach for feature map fusion. Mirror-YOLO can also produce accurate bounding polygons for instance segmentation. The effectiveness of our proposed model is demonstrated by our experiments, compared to the existing mirror detection methods, the proposed Mirror-YOLO achieves better performance in detection accuracy on the mirror image dataset.