With the rapid development of e-commerce, auto-bidding has become a key asset in optimizing advertising performance under diverse advertiser environments. The current approaches focus on reinforcement learning (RL) and generative models. These efforts imitate offline historical behaviors by utilizing a complex structure with expensive hyperparameter tuning. The suboptimal trajectories further exacerbate the difficulty of policy learning. To address these challenges, we proposes QGA, a novel Q-value regularized Generative Auto-bidding method. In QGA, we propose to plug a Q-value regularization with double Q-learning strategy into the Decision Transformer backbone. This design enables joint optimization of policy imitation and action-value maximization, allowing the learned bidding policy to both leverage experience from the dataset and alleviate the adverse impact of the suboptimal trajectories. Furthermore, to safely explore the policy space beyond the data distribution, we propose a Q-value guided dual-exploration mechanism, in which the DT model is conditioned on multiple return-to-go targets and locally perturbed actions. This entire exploration process is dynamically guided by the aforementioned Q-value module, which provides principled evaluation for each candidate action. Experiments on public benchmarks and simulation environments demonstrate that QGA consistently achieves superior or highly competitive results compared to existing alternatives. Notably, in large-scale real-world A/B testing, QGA achieves a 3.27% increase in Ad GMV and a 2.49% improvement in Ad ROI.

Automated bidding is central to modern digital advertising. Early rule-based methods lacked adaptability, while subsequent Reinforcement Learning approaches modeled bidding as a Markov Decision Process but struggled with long-term dependencies. Recent generative models show promise, yet they lack explicit mechanisms to balance exploration and safety, relying solely on action perturbations or trajectory guidance without a safety fallback. This results in inefficient exploration and elevated financial risk for advertising platforms. To address this gap, we propose GUIDE (Generative Auto-Bidding with Unified Modeling and Exploration), a framework that synergistically integrates directed exploration with a safe fallback mechanism. GUIDE employs a Decision Transformer (DT) to jointly model historical bidding actions and environmental state transitions. A Q-value module guides the DT's exploration via regularization constraints, while an Inverse Dynamics Module (IDM) leverages DT-predicted future states to infer robust, behaviorally consistent actions as a safe policy fallback. The Q-value module then adaptively selects the final action between these two options, balancing exploration and safety. Together, these components form an integrated "explore-safeguard-select" pipeline that unifies efficiency and safety. We conduct extensive experiments on public datasets, in simulated auction environments, and through large-scale online deployment on Taobao, a leading Chinese advertising platform. Results show GUIDE consistently outperforms state-of-the-art baselines across all scenarios. In real-world deployment, GUIDE achieves notable gains: +4.10% ad GMV, +1.40% ad clicks, +1.66% ad cost, and +3.52% ad ROI, demonstrating its effectiveness and strong industrial applicability.

Aligning Large Language Models (LLMs) with human feedback is crucial for their development. Existing preference optimization methods such as DPO and KTO, while improved based on Reinforcement Learning from Human Feedback (RLHF), are inherently derived from PPO, requiring a reference model that adds GPU memory resources and relies heavily on abundant preference data. Meanwhile, current preference optimization research mainly targets single-question scenarios with two replies, neglecting optimization with multiple replies, which leads to a waste of data in the application. This study introduces the MPPO algorithm, which leverages the average likelihood of model responses to fit the reward function and maximizes the utilization of preference data. Through a comparison of Point-wise, Pair-wise, and List-wise implementations, we found that the Pair-wise approach achieves the best performance, significantly enhancing the quality of model responses. Experimental results demonstrate MPPO’s outstanding performance across various benchmarks. On MT-Bench, MPPO outperforms DPO, ORPO, and SimPO. Notably, on Arena-Hard, MPPO surpasses DPO and ORPO by substantial margins. These achievements underscore the remarkable advantages of MPPO in preference optimization tasks.

The prosperous development of social e-commerce has spawned diverse recommendation demands, and accompanied a new recommendation paradigm, share recommendation. Significantly different from traditional binary recommendations (e.g., item recommendation and friend recommendation), share recommendation models ternary interactions among 〈 User, Item, Friend 〉 , which aims to recommend a most likely friend to a user who would like to share a specific item, progressively becoming an indispensable service in social e-commerce. Seamlessly integrating the social relations and purchase behaviours, share recommendation improves user stickiness and monetizes the user influence, meanwhile encountering three unique challenges: rich heterogeneous information, complex ternary interaction, and asymmetric share action. In this paper, we first study the share recommendation problem and propose a heterogeneous graph neural network based share recommendation model, called HGSRec. Specifically, HGSRec delicately designs a tripartite heterogeneous GNNs to describe the multifold characteristics of users and items, and then dynamically fuses them via capturing potential ternary dependency with a dual co-attention mechanism, followed by a transitive triplet representation to depict the asymmetry of share action and predict whether share action happens. Offline experiments demonstrate the superiority of the proposed HGSRec with significant improvements (11.7%-14.5%) over the state-of-the-arts, and online A/B testing on Taobao platform further demonstrates the high industrial practicability and stability of HGSRec.

Promotion recommendation, as a new recommendation paradigm in recent years, plays an important role in stimulating the purchase desire of users and maximizing the total revenue. Different from previous recommendations (e.g., item/group recommendation), promotion recommendation aims to select a set of K items based on all user preferences in selection phase and maximize the total revenue in delivery phase. Although these two phases are closely related with each other, existing methods usually focus on item selection in selection phase, largely ignoring the delivery phase and leading to sub-optimal performance. To solve the promotion recommendation problem, we propose the comb-K recommendation model, a constrained combinatorial optimization model which seamlessly integrates the selection phase and delivery phase with delicately designed constraints. When selecting K items, the comb-K recommendation is able to simultaneously search the optimal combination of item selection and delivery with the full consideration of all user preferences. Specifically, we propose a novel heterogeneous graph convolutional network to estimate user preference and propose the user-level comb-K recommendation model through solving a binary combination optimization problem. In order to handle combination explosion for large-scale users, we furtherly cluster massive users into limited groups and present a group-level comb-K recommendation model in which a novel heterogeneous graph pooling network is proposed to perform user clustering and estimate group preference. In addition, considering the ”long tail” phenomenon in e-commerce, we design a restricted neighbor heuristic search to accelerate the solving process. Extensive experiments on four datasets demonstrate the superiority of comb-K model for large-scale promotion recommendation. On billion-scale data, when clustering 2.5 × 107 users into 103 groups, our model is able to preserve 98.7% personalized preferences in group-level and significantly improves the Total Click and Hit Ratio by 9.35% and 7.14%, respectively.

Recently, interactive recommender systems are becoming increasingly popular. The insight is that, with the interaction between users and the system, (1) users can actively intervene the recommendation results rather than passively receive them, and (2) the system learns more about users so as to provide better recommendation.

With the prevalence of mobile e-commerce nowadays, a new type of recommendation services, called intent recommendation, is widely used in many mobile e-commerce Apps, such as Taobao and Amazon. Different from traditional query recommendation and item recommendation, intent recommendation is to automatically recommend user intent according to user historical behaviors without any input when users open the App. Intent recommendation becomes very popular in the past two years, because of revealing user latent intents and avoiding tedious input in mobile phones. Existing methods used in industry usually need laboring feature engineering. Moreover, they only utilize attribute and statistic information of users and queries, and fail to take full advantage of rich interaction information in intent recommendation, which may result in limited performances. In this paper, we propose to model the complex objects and rich interactions in intent recommendation as a Heterogeneous Information Network. Furthermore, we present a novel M etapath-guided E mbedding method for I ntent Rec ommendation~(called MEIRec). In order to fully utilize rich structural information, we design a metapath-guided heterogeneous Graph Neural Network to learn the embeddings of objects in intent recommendation. In addition, in order to alleviate huge learning parameters in embeddings, we propose a uniform term embedding mechanism, in which embeddings of objects are made up with the same term embedding space. Offline experiments on real large-scale data show the superior performance of the proposed MEIRec, compared to representative methods.Moreover, the results of online experiments on Taobao e-commerce platform show that MEIRec not only gains a performance improvement of 1.54% on CTR metric, but also attracts up to 2.66% of new users to search queries.

In e-commerce websites like Taobao, brand is playing a more important role in influencing users' decision of click/purchase, partly because users are now attaching more importance to the quality of products and brand is an indicator of quality. However, existing ranking systems are not specifically designed to satisfy this kind of demand. Some design tricks may partially alleviate this problem, but still cannot provide satisfactory results or may create additional interaction cost. In this paper, we design the first brand-level ranking system to address this problem. The key challenge of this system is how to sufficiently exploit users' rich behavior in e-commerce websites to rank the brands. In our solution, we firstly conduct the feature engineering specifically tailored for the personalized brand ranking problem and then rank the brands by an adapted Attention-GRU model containing three important modifications. Note that our proposed modifications can also apply to many other machine learning models on various tasks. We conduct a series of experiments to evaluate the effectiveness of our proposed ranking model and test the response to the brand-level ranking system from real users on a large-scale e-commerce platform, i.e. Taobao.