Existing online advertising systems employ separate models for each task and site, resulting in a large number of models that require significant computing power and human effort to train and deploy. Moreover, separate models have limitations in sharing cross-scenario information. To address these issues, we propose a unified sequential recommendation model called SCRIPT. It takes cross-scenario user behavior sequences as input and explicitly incorporates meta information that characterizes scenario features, such as domain, site, and behavior types. Inspired by the advances of the pretrain and prompt paradigm, we generate scenario-aware and personalized prompts based on the user profile and meta information of candidate items. This allows the model to leverage the knowledge learned during pre-training and adapt it to serve different downstream tasks. Extensive experiments on two public dataset and a production dataset demonstrate that our model achieves state-of-the-art performance on multiple downstream recommendation tasks.

A major challenge confronting today’s distributed metadata management schemes is how to meet the dynamic requirements of various applications through effectively mapping and migrating metadata nodes to different metadata servers (MDS’s). Most of the existing works dynamically reallocate nodes to different servers adopting history-based coarse-grained methods, failing to make a timely and efficient update on the distribution of nodes. In this paper, we present the first deep reinforcement learning-leveraged distributed metadata management scheme, AdaM, to address the aforementioned dilemma. AdaM is an adaptive fine-grained metadata management scheme that trains an actor-critic network to migrate “hot” metadata nodes to different MDS’s based on its observations of the current “states” (i.e., access pattern, the structure of namespace tree and current distribution of nodes on MDS’s). Adaptive to varying access patterns, AdaM can automatically migrate hot metadata nodes among servers to keep load balancing while maintaining metadata locality. Besides, we propose a self-adaptive metadata cache policy, which dynamically combines the two strategies of managing caches on the server side and the client side to gain better query performance. Last but not least, we design a distributed metadata processing 2PC Protocol called MST-based 2PC to ensure data consistency. Experiments on a real-world dataset demonstrate the superiority of our proposed method over other schemes.