Auto-bidding plays an essential role in online advertising, allowing agents to automatically adjust bids for advertisers. Recently, the rise of Marketing Management service in e-commerce platforms has driven the evolution from auto-bidding to auto-marketing, enabling merchants to delegate their advertising bidding and product's coupon discounting decisions to agents. Auto-marketing requires agents to jointly decide on bidding and coupon discounting. Furthermore, compared to classic static constraints, auto-marketing agent faces a self-funding constraint (where the budget for both bidding and coupon discounting is entirely derived from the agent's commission revenue). Existing rule-based or RL-based methods often struggle with dynamic environments and complex sequential dependencies. To overcome these limitations, we propose a Generative Auto-Marketing framework (GAM), designed for performing joint sequential decisions on bidding and coupon discounting, and optimizing business objectives through post-training alignment. Furthermore, GAM employs a flexible, constraint-aware reward alignment module, and utilizes Group Relative Policy Optimization (GRPO) to align the pre-trained model, thus empirically balancing objective maximization and constraint satisfaction. We construct an offline simulation environment based on large-scale real-world dataset, and demonstrate the effectiveness of GAM through extensive experimental results.

Currently, e-commerce platforms integrate ads and organic content into a mixed list for users. While platforms seek to maximize profit from advertisers, organic items enhance user experience. To ensure long-term development, platforms aim to design mechanisms that optimize both revenue and user satisfaction. Current methods rank ads and organic items separately before integrating them. Even if each part is locally optimal, the combined result may not be globally optimal. In this paper, we come up with the Joint Integrated Regret Network (JINTER Net). Unlike traditional methods, which pre-order ads and organic items separately, JINTER Net directly selects from the combined set of candidate ads and organic items to generate an optimal list. This approach aims to optimally balance platform revenue and user experience while satisfying approximate dominant strategy incentive compatibility and individual rationality. We validate the effectiveness of JINTER Net using both synthetic data and real dataset, and our experimental results show that it significantly outperforms baseline models across multiple metrics.

Recently, many e-commerce platforms have favored presenting a mixed list of ads and organic content to users. The widely-used approach separately ranks ads and organic items, then sequentially inserts ads into the list of organic items. However, this method yields sub-optimal results. Firstly, it only ensures that each generated ad and organic item list achieves local optimality, while the predetermined insertion order fails to guarantee global optimality. Secondly, this approach overlooks the mutual effect between organic items and ads, resulting in an incomplete utilization of contextual information. Besides, it cannot prevent strategic behavior by advertisers. Therefore, we propose a context-aware integrated framework to address these issues. This framework applies automated mechanism design to integrated ad auctions for the first time. Specifically, it models ads and organic items simultaneously along with their contextual information and employs a learning-based approach to prevent advertisers from engaging in strategic behavior. Afterward, the framework directly generates a mixed list, enhancing the overall performance. We also propose Transformer encoder-based Integrated Contextual Net work (TICNet) to generate the optimal integrated contextual ad auction. Finally, we validate the effectiveness of TICNet on synthetic and real-world datasets. Our experimental results demonstrate that TICNet significantly outperforms baseline models across multiple metrics.

With the continuous advancement of computer network communication technology, traditional wired and wireless networks are limited by cables and base stations, and are not applicable in some application scenarios. Therefore, mobile wireless communication methods have attracted more and more attention. Due to its dynamic topology and self-organizing without center, the self-organizing network can form a mobile temporary multi-hop mobile communication network through multiple wireless communication devices, which can well meet the above requirements. At present, research on self-organizing networks mainly focuses on routing protocols. The main types are based on network topology information and location information. This paper designs a routing algorithm based on link reliability. The algorithm fully considers the self-organizing network link information, and models the node's sending and receiving work as a Markov decision process, and uses Q-learning to demodulate. This article used NS2 for network simulation and analysis and comparison of performance indicators with traditional routing algorithms.