Multimodal information is pivotal for e-commerce search ranking. Existing works leverage multimodal data typically by fine-tuning general Multimodal Large Language Models (MLLMs) via collaborative signals, subsequently integrating the derived representations into ranking models as item features. Despite their efficacy, these methods face two primary limitations: (1) they rely on a single collaborative signal for MLLM fine-tuning, failing to exploit the heterogeneous signals essential for multitask ranking; and (2) they treat multimodal representations as regular item features in ranking models, underutilizing their latent potential for user behavior modeling. To address these challenges, we propose the Multiplex Multimodal Representation Model (MMRM), a unified framework that aligns MLLMs with diverse collaborative signals. By employing a shared backbone with task-specific tokens and projection layers, MMRM simultaneously learns from multiple signals and generates comprehensive multiplex item representations in a single inference pass. Furthermore, we introduce a multiplex user representation strategy in ranking models, which derives task-specific user representations via search-based behavior sequence modeling leveraging multiplex item representations. Extensive experiments demonstrate MMRM's superior efficiency and effectiveness. Notably, MMRM has been successfully deployed in the JD e-commerce search engine, yielding significant performance gains for millions of daily users.

User interest modeling plays a vital role in industrial recommendation systems (RSs). Existing generative recommendation (GR) methods rely on single-step direct inference, which falls short of deeply modeling complex and dynamically evolving user interest. Recent chain-of-thought (CoT)-based GR methods attempt to address this, but either suffer from information loss during semantic space transformation in explicit reasoning or yield uncontrollable, homogeneous reasoning chains in implicit reasoning.

State-of-the-art approaches for click-through rate (CTR) prediction in industry predominantly rely on transformer-based networks or their variants. However, as user behavior sequences become longer, employing self-attention networks for CTR prediction within a constrained inference time presents a significant challenge. To address this, mainstream methods adopt a classical two-stage paradigm: a General Search Unit (GSU) for quickly retrieving relevant items from long-term behaviors, and an Exact Search Unit (ESU) for applying effective Multi-Head Target Attention (MHTA) over the items selected by the GSU. These two-stage algorithms have certain limitations. Firstly, the GSU needs to retrieve different target subsequences for different target items, restricting the ESU to a suboptimal MHTA network rather than a more effective transformer-based network. Secondly, the GSU retrieves only a subset of items from the user's behavior sequence, ignoring the evolution of user interests and the interrelationships between different points of interest. To overcome these challenges, we propose a novel end-to-end hierarchical user long-term behavior modeling network for CTR prediction (HBM). Specifically, we employ the multi-interest routing layer to channel the user's long-term behavior to several aggregated interest clusters. Furthermore, we introduce a fine interest learning network that selects the top-k interests from the initial aggregated representations. Subsequently, we employ a transformer network to model the user's behavior sequence associated with these top-k interests in a detailed manner, while also capturing the inherent correlations between different user interests at a coarse level. Finally, we integrate the coarse and fine interests. Extensive experiments on two real-world datasets demonstrate the effectiveness of our proposed methods. In addition, an online A/B test on the JD recommendation platform shows promising improvements, with a 2.15% increase in CTR and a 0.98% increase in CVR, accompanied by lower online inference latency.

In the dynamic landscape of online advertising, decoding user intent remains a pivotal challenge, particularly in the context of query classification. Swift classification models, exemplified by FastText, cater to the demand for real-time responses but encounter limitations in handling intricate queries. Conversely, accuracy-centric models like BERT introduce challenges associated with increased latency. This paper undertakes a nuanced exploration, navigating the delicate balance between efficiency and accuracy. It unveils FastText's latent potential as an 'online dictionary' for historical queries while harnessing the semantic robustness of BERT for novel and complex scenarios. The proposed Distribution-Diverse Multi-Expert (DDME) framework employs multiple teacher models trained from diverse data distributions. Through meticulous data categorization and enrichment, it elevates the classification performance across the query spectrum. Empirical results within the JD ads search system validate the superiority of our proposed approaches.

Click-Through Rate (CTR) prediction plays a pivotal role in numerous industrial applications, including online advertising and recommender systems. Existing approaches primarily focus on modeling the correlation between user interests and candidate items. However, we argue that personalized user preferences for candidate items depend not only on correlation but also on the satisfaction of associated interests. To address this limitation, we propose SUIN, a novel CTR model that integrates satisfaction factors into user interest modeling for enhanced click-through rate prediction. Specifically, we employ a user interest satisfaction-aware network to capture the degree of satisfaction for each interest, thereby enabling adaptation of the user's personalized preference based on satisfaction levels. Additionally, we leverage the exposure-unclicked signal (recommended to the user but not clicked) as supervision during training, facilitating the interest satisfaction module to better model the satisfaction degree of user interests. Besides, this module serves as a foundational building block suitable for integration into mainstream sequential-based CTR models. Extensive experiments conducted on two real-world datasets demonstrate the superiority of our proposed model, outperforming state-of-the-art methods across various evaluation metrics. Furthermore, an online A/B test deployed on large-scale recommender systems shows significant improvements achieved by our model in diverse evaluation metrics.

Click-Through Rate (CTR) prediction is becoming increasingly vital in many industrial applications, such as recommendations and online advertising. How to precisely capture users' dynamic and evolving interests from previous interactions (e.g., clicks, purchases, etc.) is a challenging task in CTR prediction. Mainstream approaches focus on disentangling user interests in a heuristic way or modeling user interests into a static representation. However, these approaches overlook the importance of users' current intent and the complex interactions between their current intent and global interests. To address these concerns, in this paper, we propose a novel intent-enhanced user interest modeling for click-through rate prediction in large-scale e-commerce recommendations, abbreviated as IUI. Methodologically, different from existing works, we consider users' recent interactions to be inspired by their implicit intent and then leverage an intent-aware network to model their current local interests in a more precise and fine-grained manner. In addition, to obtain a more stable co-dependent global and local interest representation, we employ a co-attention network capable of activating the corresponding interest in global-level interactions and capturing the dynamic interactions between global- and local-level interaction behaviors. Finally, we incorporate self-supervised learning into the model training by maximizing the mutual information between the global and local representations obtained via the above two networks to enhance the CTR prediction performance. Compared with existing methods, IUI benefits from the different granularity of user interest to generate a more accurate and comprehensive preference representation. Experimental results demonstrate that the proposed model outperforms previous state-of-the-art methods in various metrics on three real-world datasets. In addition, an online A/B test deployed on the JD recommendation platforms shows a promising improvement across multiple evaluation metrics.

Recently, Graph Convolutional Network (GCN) based methods have become novel state-of-the-arts for Collaborative Filtering (CF) based Recommender Systems. To obtain users' preferences over different items, it is a common practice to learn representations of users and items by performing embedding propagation on a user-item bipartite graph, and then calculate the preference scores based on the representations. However, in most existing algorithms, user/item representations are generated independently of target items/users. To address this problem, we propose a novel graph attention model named Bilateral Interactive GCN (BI-GCN), which introduces bilateral interactive guidance into each user-item pair and thus leads to target-aware representations for preference prediction. Specifically, to learn the user/item representation from its neighborhood, we assign higher attention weights to those neighbors similar to the target item/user. By this manner, we can obtain target-aware representations, i.e., the information of the target item/user is explicitly encoded in the corresponding user/item representation, for more precise matching. Extensive experiments on three benchmark datasets demonstrate the effectiveness and robustness of BI-GCN.

Thompson Sampling (TS) is an effective way to deal with the exploration-exploitation dilemma for the multi-armed (contextual) bandit problem. Due to the sophisticated relationship between contexts and rewards in real- world applications, neural networks are often preferable to model this relationship owing to their superior representation capacity. In this paper, we study the problem of combining neural networks with TS in a plug-and-play manner. The basic idea is to maintain a posterior distribution over the reward mean relying on the prediction and the deep representation of the neural network for any given context. Specifically, our proposed algorithm, PlugTS (Pluggable deep Thompson Sampling), introduces no change into the network training process, but only requires one additional sampling stage during serving - sampling from a univariate Gaussian distribution (by maintaining a positive definite matrix). Theoretically, we prove that PlugTS achieves an regret bound, which matches the state-of-the-art neural network-based TS, while PlugTS enjoys much lower computational overhead for each iteration. Experimental results on public datasets among traditional classification and recommendation tasks validate the effectiveness and efficiency of PlugTS. Furthermore, it is inspiring for real-world applications that a simplified version of PlugTS has been deployed in an industrial advertising recommender system of one of the world's largest e-commerce platforms, JD.com, achieving significant improvement in both RPM (Revenue Per Mille) and CTR (Click-Through Rate) in online A/B testing. The appendix and code are available at https://github.com/adsturing/PlugTS.

Organic recommendation and advertising recommendation usually coexist on e-commerce platforms. In this paper, we study the problem of utilizing data from organic recommendation to reinforce click-through rate prediction in advertising scenarios from a multi-view learning perspective. We propose a novel method, termed LOVF (Layered Organic View Fusion). LOVF implements a multi-view fusion mechanism - for each advertising instance, LOVF derives deep representations layer-by-layer from the organic recommendation view and these deep representations are then fused into the corresponding vanilla representations of the advertising view. Extensive experiments across a variety of backbones demonstrate LOVF's generality, effectiveness and efficiency on a new real-world production dataset. The dataset encompasses data from both the organic recommendation and advertising scenarios. Notably, LOVF has been successfully deployed in the advertising recommender system of JD.com, which is one of the world's largest e-commerce platforms; online A/B testing shows that LOVF achieves impressive improvement on advertising clicks and revenue. Our code and dataset are available at https://github.com/adsturing/lovf for facilitating further research.

CTR prediction is crucial in recommendation systems and online advertising platforms, where user-generated data streams that drift over time can lead to catastrophic forgetting if the model continuously adapts to new data distribution. Conventional strategies for catastrophic forgetting are challenging to deploy due to memory constraints and diverse data distributions. To address this, we propose a novel drift-aware incremental learning framework based on ensemble learning for CTR prediction, which uses explicit error-based drift detection on streaming data to strengthen well-adapted ensembles and freeze ensembles that do not match the input distribution, avoiding catastrophic interference. Our method outperforms all baselines considered in offline experiments and A/B tests.

Click-through rate (CTR) Prediction is of great importance in real-world online ads systems. One challenge for the CTR prediction task is to capture the real interest of users from their clicked items, which is inherently influenced by presented positions of items, i.e., more front positions tend to obtain higher CTR values. Therefore, It is crucial to make CTR models aware of the exposed position of the items. A popular line of existing works focuses on explicitly model exposed position by result randomization which is expensive and inefficient, or by inverse propensity weighting (IPW) which relies heavily on the quality of the propensity estimation. Another common solution is modeling position as features during offline training and simply adopting fixed value or dropout tricks when serving. However, training-inference inconsistency can lead to sub-optimal performance. This work proposes a simple yet efficient knowledge distillation framework to model the impact of exposed position and leverage position information to improve CTR prediction. We demonstrate the performance of our proposed method on a real-world production dataset and online A/B tests, achieving significant improvements over competing baseline models. The proposed method has been deployed in the real world online ads systems of JD, serving main traffic of hundreds of millions of active users.

Click-through rate (CTR) prediction plays a crucial role in sponsored search advertising (search ads). User click behavior usually showcases strong comparison patterns among relevant/competing items within the user awareness. Explicit user awareness could be characterized by user behavior sequence modeling, which however suffers from issues such as cold start, behavior noise and hidden channels. Instead, in this paper, we study the problem of modeling implicit user awareness about relevant/competing items. We notice that candidate items of the CTR prediction model could play as surrogates for relevant/competing items within the user awareness. Motivated by this finding, we propose a novel framework, named CIM (Candidate Item Modeling), to characterize users’ awareness on candidate items. CIM introduces an additional module to encode candidate items into a context vector and therefore is plug-and-play for existing neural network-based CTR prediction models. Offline experiments on a ten-billion-scale production dataset collected from the real traffic of a search advertising system, together with the corresponding online A/B testing, demonstrate CIM’s superior performance. Notably, CIM has been deployed in production at JD.com, serving the main traffic of hundreds of millions of users, which shows great application value. Our code and dataset are available at https://github.com/kaifuzheng/cim.

Click-through rate (CTR) prediction based on deep neural networks has made significant progress in recommendation systems. However, these methods often suffer from CTR underestimation due to insufficient impressions for long-tail items. When formalizing CTR prediction as a contextual bandit problem, exploration methods provide a natural solution addressing this issue. In this paper, we first benchmark state-of-the-art exploration methods in the recommendation system setting. We find that the combination of gradient-based uncertainty modeling and Thompson Sampling achieves a significant advantage. On the basis of the benchmark, we further propose a general enhancement strategy, Underestimation Refinement (UR), which explicitly incorporates the prior knowledge that insufficient impressions likely leads to CTR underestimation. This strategy is applicable to almost all the existing exploration methods. Experimental results validate UR's effectiveness, achieving consistent improvement across all baseline exploration methods.

As one of the largest B2C e-commerce platforms in China, JD.com also powers a leading advertising system, serving millions of advertisers with fingertip connection to hundreds of millions of customers. In our system, as well as most e-commerce scenarios, ads are displayed with images. This makes visual-aware Click Through Rate (CTR) prediction of crucial importance to both business effectiveness and user experience. Existing algorithms usually extract visual features using off-the-shelf Convolutional Neural Networks (CNNs) and late fuse the visual and non-visual features for the finally predicted CTR. Despite being extensively studied, this field still face two key challenges. First, although encouraging progress has been made in offline studies, applying CNNs in real systems remains non-trivial, due to the strict requirements for efficient end-to-end training and low-latency online serving. Second, the off-the-shelf CNNs and late fusion architectures are suboptimal. Specifically, off-the-shelf CNNs were designed for classification thus never take categories as input features. While in e-commerce, categories are precisely labeled and contain abundant visual priors that will help the visual modeling. Unaware of the ad category, these CNNs may extract some unnecessary category-unrelated features, wasting CNN's limited expression ability. To overcome the two challenges, we propose Category-specific CNN (CSCNN) specially for CTR prediction. CSCNN early incorporates the category knowledge with a light-weighted attention-module on each convolutional layer. This enables CSCNN to extract expressive category-specific visual patterns that benefit the CTR prediction. Offline experiments on benchmark and a 10 billion scale real production dataset from JD, together with an Online A/B test show that CSCNN outperforms all compared state-of-the-art algorithms. We also build a highly efficient infrastructure to accomplish end-to-end training with CNN on the 10 billion scale real production dataset within 24 hours, and meet the low latency requirements of online system (20ms on CPU). CSCNN is now deployed in the search advertising system of JD, serving the main traffic of hundreds of millions of active users.