Graph-based Recommendation Systems (GRSs) have gained prominence for their ability to enhance the accuracy and effectiveness of recommender systems by exploiting structural relationships in user-item interaction data. Despite their advanced capabilities, we find GRSs are susceptible to feedback-loop phenomena that disproportionately diminish the visibility of new and long-tail items, leading to a homogenization of recommendations and the potential emergence of echo chambers. To mitigate this feedback-loop issue, exploration and exploitation (E&E) strategies have been extensively researched. However, conventional E&E methods rest on the assumption that recommendations are independent and identically distributed-an assumption that is not valid for GRSs. To forge an effective E&E approach tailored to GRSs, we introduce a novel framework, the GRADient-informed Exploration and Exploitation (GRADE), designed to adaptively seek out underrepresented or new items with promising rewards. Our method evaluates the potential benefit of exploring an item by assessing the change in the system's empirical risk error pre- and post-exposure. For practical implementation, we approximate this measure using the gradients of potential edges and model parameters, alongside their associated uncertainties. We then orchestrate the balance between exploration and exploitation utilizing Thompson sampling and the Upper Confidence Bound (UCB) strategy. Empirical tests on datasets from two industrial environments demonstrate that GRADE consistently outperforms existing state-of-the-art methods. Additionally, our approach has been successfully integrated into actual industrial systems.

Recommendation systems are widely used in e-commerce websites and online platforms to address information overload. However, existing systems primarily rely on historical data and user feedback, making it difficult to capture user intent transitions. Recently, Knowledge Base (KB)-based models are proposed to incorporate expert knowledge, but it struggle to adapt to new items and the evolving e-commerce environment. To address these challenges, we propose a novel Large Language Model based Complementary Knowledge Enhanced Recommendation System (LLM-KERec). It introduces an entity extractor that extracts unified concept terms from item and user information. To provide cost-effective and reliable prior knowledge, entity pairs are generated based on entity popularity and specific strategies. The large language model determines complementary relationships in each entity pair, constructing a complementary knowledge graph. Furthermore, a new complementary recall module and an Entity-Entity-Item (E-E-I) weight decision model refine the scoring of the ranking model using real complementary exposure-click samples. Extensive experiments conducted on three industry datasets demonstrate the significant performance improvement of our model compared to existing approaches. Additionally, detailed analysis shows that LLM-KERec enhances users' enthusiasm for consumption by recommending complementary items. In summary, LLM-KERec addresses the limitations of traditional recommendation systems by incorporating complementary knowledge and utilizing a large language model to capture user intent transitions, adapt to new items, and enhance recommendation efficiency in the evolving e-commerce landscape.

Modern recommendation platforms frequently encompass multiple domains to cater to the varied preferences of users. Recently, cross-domain learning has gained traction as a significant paradigm within the context of recommendation systems, enabling the leveraging of rich information from a well-endowed source domain to enhance a target domain, often limited by inadequate data resources. A primary concern in cross-domain recommendation is the mitigation of negative transfer-ensuring the selective transference of pertinent knowledge from the source (domain-shared knowledge) while maintaining the integrity of domain-unique insights within the target domain (domain-specific knowledge).

Model-free RL-based recommender systems have recently received increasing research attention due to their capability to handle partial feedback and long-term rewards. However, most existing research has ignored a critical feature in recommender systems: one user's feedback on the same item at different times is random. The stochastic rewards property essentially differs from that in classic RL scenarios with deterministic rewards, which makes RL-based recommender systems much more challenging. In this paper, we first demonstrate in a simulator environment where using direct stochastic feedback results in a significant drop in performance. Then to handle the stochastic feedback more efficiently, we design two stochastic reward stabilization frameworks that replace the direct stochastic feedback with that learned by a supervised model. Both frameworks are model-agnostic, i.e., they can effectively utilize various supervised models. We demonstrate the superiority of the proposed frameworks over different RL-based recommendation baselines with extensive experiments on a recommendation simulator as well as an industrial-level recommender system.

In marketing recommendations, the campaign organizers will distribute coupons to users to encourage consumption. In general, a series of strategies are employed to interfere with the coupon distribution process, leading to a growing imbalance between user-coupon interactions, resulting in a bias in the estimation of conversion probabilities. We refer to the estimation bias as the matching bias. In this paper, we explore how to alleviate the matching bias from the causal-effect perspective. We regard the historical distributions of users and coupons over each other as confounders and characterize the matching bias as a confounding effect to reveal and eliminate the spurious correlations between user-coupon representations and conversion probabilities. Then we propose a new training paradigm named De-Matching Bias Recommendation (DMBR) to remove the confounding effects during model training via the backdoor adjustment. We instantiate DMBR on two representative models: DNN and MMOE, and conduct extensive offline and online experiments to demonstrate the effectiveness of our proposed paradigm.

We study the budget allocation problem in online marketing campaigns that utilize previously collected offline data. We first discuss the long-term effect of optimizing marketing budget allocation decisions in the offline setting. To overcome the challenge, we propose a novel game-theoretic offline value-based reinforcement learning method using mixed policies. The proposed method reduces the need to store infinitely many policies in previous methods to only constantly many policies, which achieves nearly optimal policy efficiency, making it practical and favorable for industrial usage. We further show that this method is guaranteed to converge to the optimal policy, which cannot be achieved by previous value-based reinforcement learning methods for marketing budget allocation. Our experiments on a large-scale marketing campaign with tens-of-millions users and more than one billion budget verify the theoretical results and show that the proposed method outperforms various baseline methods. The proposed method has been successfully deployed to serve all the traffic of this marketing campaign.

Modeling users' historical behaviors is an essential task in many industrial recommender systems. The user interest representation, in previous works, is obtained through the following paradigm: concrete behaviors are firstly embedded as low-dimensional behavior representations, which are then aggregated conditioning on the target item for final user interest representation. Most existing researches focus on the aggregation process that explores the intrinsic structure of the behavior sequences. However, the quality of behavior representation is largely ignored. In this paper, we present a pluggable module, FwSeqBlock, to enhance the expressiveness of behavior representations. Specifically, FwSeqBlock introduces the multiplicative operation among users' historical behaviors and the target item, where a field memory unit is designed to dynamically identify the dominant features from the behavior sequence and filter out the noise. Extensive experiments validate that FwSeqBlock consistently generates higher-quality user representations compared with competitive methods. Besides, online A/B testing reports a 4.46% improvement in Click-Through Rate (CTR), confirming the effectiveness of the proposed method.

There exists the cold-start problem in the recommendation systems when observed user-item interactions are insufficient. To alleviate this problem, most existing works aim to learn globally shared prior knowledge across all items and be fast adapted to a new item with few interactions. However, such learning techniques are data demanding and work poorly on new items with no interactions. In this applied paper, we present an industrial framework recently deployed on Alipay to address the item cold-start problem in zero-shot scenarios. The proposed framework provides both efficient and high-quality recommendations for cold items with no log data. Specifically, we formulate the cold-start problem as a zero-shot learning problem and build a highly efficient infrastructure to accomplish online zero-shot recommendations used on large-scale platforms. Extensive offline experiments and online A/B testing demonstrate that the proposed framework has superior performance and recommends cold items to preferred users more effectively than other state-of-the-art methods.

Modern recommendation systems introduce the re-ranking stage to optimize the entire list directly. This paper focuses on the design of re-ranking framework in feed to optimally model the mutual influence between items and further promote user engagement. On mobile devices, users browse the feed almost in a top-down manner and rarely compare items back and forth. Besides, users often compare item with its adjacency based on their partial observations. Given the distinct user behavior patterns, the modeling of mutual influence between items should be carefully designed. Existing re-ranking models encode the mutual influence between items with sequential encoding methods. However, previous works may be dissatisfactory due to the ignorance of connections between items on different scopes. In this paper, we first discuss Unidirectivity and Locality on the impacts and consequences, then report corresponding solutions in industrial applications. We propose a novel framework based on the empirical evidence from user analysis. To address the above problems, we design a \underlineS cope-aware \underlineR e-ranking with \underlineG ated \underlineA ttention model (SRGA ) to emulate the user behavior patterns from two aspects: 1) we emphasize the influence along the user's common browsing direction; 2) we strength the impacts of pivotal adjacent items within the user visual window. Specifically, we design a global scope attention to encode inter-item patterns unidirectionally from top to bottom. Besides, we devise a local scope attention sliding over the recommendation list to underline interactions among neighboring items. Furthermore, we design a learned gate mechanism to aggregating the information dynamically from local and global scope attention. Extensive offline experiments and online A/B testing demonstrate the benefits of our novel framework. The proposed SRGA model achieves the best performance in offline metrics compared with the state-of-the-art re-ranking methods. Further, empirical results on live traffic validate that our recommender system, equipped with SRGA in the re-ranking stage, improves significantly in user engagement.

Many payment platforms hold large-scale marketing campaigns, which allocate incentives to encourage users to pay through their applications. To maximize the return on investment, incentive allocations are commonly solved in a two-stage procedure. After training a response estimation model to estimate the users' mobile payment probabilities (MPP), a linear programming process is applied to obtain the optimal incentive allocation. However, the large amount of biased data in the training set, generated by the previous biased allocation policy, causes a biased estimation. This bias deteriorates the performance of the response model and misleads the linear programming process, dramatically degrading the performance of the resulting allocation policy. To overcome this obstacle, we propose a bias correction adversarial network. Our method leverages the small set of unbiased data obtained under a full-randomized allocation policy to train an unbiased model and then uses it to reduce the bias with adversarial learning. Offline and online experimental results demonstrate that our method outperforms state-of-the-art approaches and significantly improves the performance of the resulting allocation policy in a real-world marketing campaign.