As a local-based service (LBS), search ad retrieval in online meal delivery platforms should be broader to bridge the gap between vague consumption intentions of users and shortage of ad candidates limited by users' queries and positions. Recently, graph neural networks (GNNs) have been successfully applied to search ad retrieval task. However, directly applying GNNs suffer from noisy interactions and intents indistinguishability, which seriously degrades systems' effectiveness in the broad retrieval. In this paper, we propose a Language-EnhAnced Denoising and Intent Distinguishing graph neural network, LEAD-ID, which is developed and deployed at Meituan for sponsored search broad retrieval. To denoise interaction data, LEAD-ID designs hard- and soft- denoising strategies for GNNs based on a pretrained language model. A variational EM method is also employed to reduce high computational complexity of combining LMs and GNNs jointly. To distinguish various intents, LEAD-ID generates intent-aware node representations based on meticulously crafted LMs (language model) and GNNs; and then, it is guided by a contrastive learning object in an explicit and effective manner. According to offline experiments and online A/B tests, our framework significantly outperforms baselines in terms of recall and revenue.

Re-ranking draws increased attention on both academics and industries, which rearranges the ranking list by modeling the mutual influence among items to better meet users' demands. Many existing re-ranking methods directly take the initial ranking list as input, and generate the optimal permutation through a well-designed context-wise model, which brings the evaluation-before-reranking problem. Meanwhile, evaluating all candidate permutations brings unacceptable computational costs in practice. Thus, to better balance efficiency and effectiveness, online systems usually use a two-stage architecture which uses some heuristic methods such as beam-search to generate a suitable amount of candidate permutations firstly, which are then fed into the evaluation model to get the optimal permutation. However, existing methods in both stages can be improved through the following aspects. As for generation stage, heuristic methods only use point-wise prediction scores and lack an effective judgment. As for evaluation stage, most existing context-wise evaluation models only consider the item context and lack more fine-grained feature context modeling.

In the recommendation systems, there are multiple business domains to meet the diverse interests and needs of users, and the click-through rate(CTR) of each domain can be quite different, which leads to the demand for CTR prediction modeling for different business domains. The industry solution is to use domain-specific models or transfer learning techniques for each domain. The disadvantage of the former is that the data from other domains is not utilized by a single domain model, while the latter leverage all the data from different domains, but the fine-tuned model of transfer learning may trap the model in a local optimum of the source domain, making it difficult to fit the target domain. Meanwhile, significant differences in data quantity and feature schemas between different domains, known as domain shift, may lead to negative transfer in the process of transferring. To overcome these challenges, we propose the Collaborative Cross-Domain Transfer Learning Framework (CCTL). CCTL evaluates the information gain of the source domain on the target domain using a symmetric companion network and adjusts the information transfer weight of each source domain sample using the information flow network. This approach enables full utilization of other domain data while avoiding negative migration. Additionally, a representation enhancement network is used as an auxiliary task to preserve domain-specific features. Comprehensive experiments on both public and real-world industrial datasets, CCTL achieved SOTA score on offline metrics. At the same time, the CCTL algorithm has been deployed in Meituan, bringing 4.37% CTR and 5.43% GMV lift, which is significant to the business.

User behavior sequence modeling plays a significant role in Click-Through Rate (CTR) prediction on e-commerce platforms. Except for the interacted items, user behaviors contain rich interaction information, such as the behavior type, time, location, etc. However, so far, the information related to user behaviors has not yet been fully exploited. In the paper, we propose the concept of a situation and situational features for distinguishing interaction behaviors and then design a CTR model named Deep Situation-Aware Interaction Network (DSAIN). DSAIN first adopts the reparameterization trick to reduce noise in the original user behavior sequences. Then it learns the embeddings of situational features by feature embedding parameterization and tri-directional correlation fusion. Finally, it obtains the embedding of behavior sequence via heterogeneous situation aggregation. We conduct extensive offline experiments on three real-world datasets. Experimental results demonstrate the superiority of the proposed DSAIN model. More importantly, DSAIN has increased the CTR by 2.70%, the CPM by 2.62%, and the GMV by 2.16% in the online A/B test. Now, DSAIN has been deployed on the Meituan food delivery platform and serves the main traffic of the Meituan takeout app. Our source code is available at https://github.com/W-void/DSAIN.

Click-through rate (CTR) prediction plays a critical role in various online applications, aiming to estimate the user's click probability. User interest modeling from various interactive behaviors(e.g., click, add-to-cart, order) is becoming a mainstream approach to CTR prediction. We argue that the various user behaviors contain two important intrinsic characteristics: 1) The discrepancy in various behaviors reveals different aspects of user's behavior-specific interests. For example, one may click out of need but pay more attention to the rating when purchasing. 2) The consistency of various behaviors contains user's behavior-invariant interest. For example, the user prefers interacted items rather than other items. Therefore, it is necessary to disentangle the discrepancy and consistency signals from the massive behavior information. Unfortunately, previous methods have yet to study this phenomenon well, which limits the recommendation performance.

Click-through rate (CTR) prediction plays a crucial role in industrial recommendation and advertising systems, which generate and expose multiple items for each user request. Although the user's click action on an item will be affected by the other exposed items (called contextual items), current CTR prediction methods do not exploit this context because CTR prediction is performed before the contextual items are generated. This paper introduces a solution Contextual Items Simulation and Modeling (CISM) to tackle this limitation. Specifically, we propose a near-line Context Simulation Center to simulate exposure page without affecting online service latency, and an online Context Modeling Transformer to learn user-wise context from the simulated results w.r.t. the candidate item. In addition, knowledge distillation is introduced to further improve CTR prediction. Extensive experiments on both public and industrial datasets demonstrate the effectiveness of CISM. Currently, CISM has been deployed in the online display advertising system of Meituan Waimai, serving the main traffic.

Nowadays, the mainstream approach in position allocation system is to utilize a reinforcement learning model to allocate appropriate locations for items in various channels and then mix them into the feed. There are two types of data employed to train reinforcement learning (RL) model for position allocation, named strategy data and random data. Strategy data is collected from the current online model, it suffers from an imbalanced distribution of stateaction pairs, resulting in severe overestimation problems during training. On the other hand, random data offers a more uniform distribution of state-action pairs, but is challenging to obtain in industrial scenarios as it could negatively impact platform revenue and user experience due to random exploration. As the two types of data have different distributions, designing an effective strategy to leverage both types of data to enhance the efficacy of the RL model training has become a highly challenging problem. In this study, we propose a framework namedMulti-Distribution Data Learning (MDDL) to address the challenge of effectively utilizing both strategy and random data for training RL models on mixed multi-distribution data. Specifically, MDDL incorporates a novel imitation learning signal to mitigate overestimation problems in strategy data and maximizes the RL signal for random data to facilitate effective learning. In our experiments, we evaluated the proposed MDDL framework in a real-world position allocation system and demonstrated its superior performance compared to the previous baseline. MDDL has been fully deployed on the Meituan food delivery platform and currently serves over 300 million users.

The repeated user-item interaction now is becoming a common phenomenon in the e-commerce scenario. Due to its potential economic profit, various models are emerging to predict which item will be re-interacted based on the user-item interactions. In this specific scenario, item relevance is a critical factor that needs to be concerned, which tends to have different effects on the succeeding re-interacted one (i.e., stimulating or delaying its emergence). It is necessary to make a detailed discernment of item relevance for a better repetition-aware recommendation. Unfortunately, existing works usually mixed all these types, which may disturb the learning process and result in poor performance.

Recommender systems aim to recommend the most suitable items to users from a large number of candidates. Their computation cost grows as the number of user requests and the complexity of services (or models) increases. Under the limitation of computation resources (CRs), how to make a trade-off between computation cost and business revenue becomes an essential question. The existing studies focus on dynamically allocating CRs in queue truncation scenarios (i.e., allocating the size of candidates), and formulate the CR allocation problem as an optimization problem with constraints. Some of them focus on single-phase CR allocation, and others focus on multi-phase CR allocation but introduce some assumptions about queue truncation scenarios. However, these assumptions do not hold in other scenarios, such as retrieval channel selection and prediction model selection. Moreover, existing studies ignore the state transition process of requests between different phases, limiting the effectiveness of their approaches.

Click-through rate (CTR) prediction aims to predict the probability that the user will click an item, which has been one of the key tasks in online recommender and advertising systems. In such systems, rich user behavior (viz. long- and short-term) has been proved to be of great value in capturing user interests. Both industry and academy have paid much attention to this topic and propose different approaches to modeling with long-term and short-term user behavior data. But there are still some unresolved issues. More specially, (1) rule and truncation based methods to extract information from long-term behavior are easy to cause information loss, and (2) single feedback behavior regardless of scenario to extract information from short-term behavior lead to information confusion and noise. To fill this gap, we propose a Graph based Long-term and Short-term interest Model, termed GLSM. It consists of a multi-interest graph structure for capturing long-term user behavior, a multi-scenario heterogeneous sequence model for modeling short-term information, then an adaptive fusion mechanism to fused information from long-term and short-term behaviors. Comprehensive experiments on real-world datasets, GLSM achieved SOTA score on offline metrics. At the same time, the GLSM algorithm has been deployed in our industrial application, bringing 4.9% CTR and 4.3% GMV lift, which is significant to the business

With the recent prevalence of reinforcement learning (RL), there have been tremendous interests in utilizing RL for ads allocation in recommendation platforms (e.g., e-commerce and news feed sites). To achieve better allocation, the input of recent RL-based ads allocation methods is upgraded from point-wise single item to list-wise item arrangement. However, this also results in a high-dimensional space of state-action pairs, making it difficult to learn list-wise representations with good generalization ability. This further hinders the exploration of RL agents and causes poor sample efficiency. To address this problem, we propose a novel RL-based approach for ads allocation which learns better list-wise representations by leveraging task-specific signals on Meituan food delivery platform. Specifically, we propose three different auxiliary tasks based on reconstruction, prediction, and contrastive learning respectively according to prior domain knowledge on ads allocation. We conduct extensive experiments on Meituan food delivery platform to evaluate the effectiveness of the proposed auxiliary tasks. Both offline and online experimental results show that the proposed method can learn better list-wise representations and achieve higher revenue for the platform compared to the state-of-the-art baselines.

Ads allocation, which involves allocating ads and organic items to limited slots in feed with the purpose of maximizing platform revenue, has become a research hotspot. Notice that, platforms (e.g., e-commerce platforms, video platforms, food delivery platforms and so on) usually have multiple entrances for different categories and some entrances have few visits. Data from these entrances has low coverage, which makes it difficult for the agent to learn. To address this challenge, we propose Similarity-based Hybrid Transfer for Ads Allocation (SHTAA), which effectively transfers samples as well as knowledge from data-rich entrance to data-poor entrance. Specifically, we define an uncertainty-aware similarity for MDP to estimate the similarity of MDP for different entrances. Based on this similarity, we design a hybrid transfer method, including instance transfer and strategy transfer, to efficiently transfer samples and knowledge from one entrance to another. Both offline and online experiments on Meituan food delivery platform demonstrate that the proposed method could achieve better performance for data-poor entrance and increase the revenue for the platform.

A mixed list of ads and organic items is usually displayed in feed and how to allocate the limited slots to maximize the overall revenue is a key problem. Meanwhile, user behavior modeling is essential in recommendation and advertising (e.g., CTR prediction and ads allocation). Most previous works only model point-level positive feedback (i.e., click), which neglect the page-level information of feedback and other types of feedback. To this end, we propose Deep Page-level Interest Network (DPIN) to model the page-level user preference and exploit multiple types of feedback. Specifically, we introduce four different types of page-level feedback, and capture user preference for item arrangement under different receptive fields through the multi-channel interaction module. Through extensive offline and online experiments on Meituan food delivery platform, we demonstrate that DPIN can effectively model the page-level user preference and increase the revenue.

With the rapid growth of interaction data, many clustering methods have been proposed to discover interaction patterns as prior knowledge beneficial to downstream tasks. Considering that an interaction can be seen as an action occurring among multiple objects, most existing methods model the objects and their pair-wise relations as nodes and links in graphs. However, they only model and leverage part of the information in real entire interactions, i.e., either decompose the entire interaction into several pair-wise sub-interactions for simplification, or only focus on clustering some specific types of objects, which limits the performance and explainability of clustering. To tackle this issue, we propose to Co-cluster the Interactions via Attentive Hypergraph neural network (CIAH). Particularly, with more comprehensive modeling of interactions by hypergraph, we propose an attentive hypergraph neural network to encode the entire interactions, where an attention mechanism is utilized to select important attributes for explanations. Then, we introduce a salient method to guide the attention to be more consistent with real importance of attributes, namely saliency-based consistency. Moreover, we propose a novel co-clustering method to perform a joint clustering for the representations of interactions and the corresponding distributions of attribute selection, namely cluster-based consistency. Extensive experiments demonstrate that our CIAH significantly outperforms state-of-the-art clustering methods on both public datasets and real industrial datasets.

E-commerce platforms usually display a mixed list of ads and organic items in feed. One key problem is to allocate the limited slots in the feed to maximize the overall revenue as well as improve user experience, which requires a good model for user preference. Instead of modeling the influence of individual items on user behaviors, the arrangement signal models the influence of the arrangement of items and may lead to a better allocation strategy. However, most of previous strategies fail to model such a signal and therefore result in suboptimal performance. In addition, the percentage of ads exposed (PAE) is an important indicator in ads allocation. Excessive PAE hurts user experience while too low PAE reduces platform revenue. Therefore, how to constrain the PAE within a certain range while keeping personalized recommendation under the PAE constraint is a challenge.

Recent advances in click model have established it as an attractive approach to infer document relevance. Most of these advances consider the user click/skip behavior as binary events but neglect the context in which a click happens. We show that real click behavior in industrial search engines is often noisy and not always a good indication of relevance. For a considerable percentage of clicks, users select what turn out to be irrelevant documents and these clicks should not be directly used as evidence for relevance inference. Thus in this paper, we put forward an observation that the relevance indication degree of a click is not a constant, but can be differentiated by user preferences and the context in which the user makes her click decision. In particular, to interpret the click behavior discriminatingly, we propose a Noise-aware Click Model (NCM) by characterizing the noise degree of a click, which indicates the quality of the click for inferring relevance. Specifically, the lower the click noise is, the more important the click is in its role for relevance inference. To verify the necessity of explicitly accounting for the uninformative noise in a user click, we conducted experiments on a billion-scale dataset. Extensive experimental results demonstrate that as compared with two state-of-the-art click models in Web Search, NCM can better interpret user click behavior and achieve significant improvements in terms of both perplexity and NDCG.

Recent advances in search users' click modeling consider both users' search queries and click/skip behavior on documents to infer the user's perceived relevance. Most of these models, including dynamic Bayesian networks (DBN) and user browsing models (UBM), use probabilistic models to understand user click behavior based on individual queries. The user behavior is more complex when her actions to satisfy her information needs form a search session, which may include multiple queries and subsequent click behaviors on various items on search result pages. Previous research is limited to treating each query within a search session in isolation, without paying attention to their dynamic interactions with other queries in a search session.

Recent advances in click model have positioned it as an effective approach to estimate document relevance based on user behavior in web search. Yet, few works have been conducted to explore the use of click model to help web search ranking. In this paper, we focus on learning a ranking function by taking the results from a click model into account. Thus, besides the editorial relevance data arising from the explicit manually labeled search result by experts, we also have the estimated relevance data that is automatically inferred from click models based on user search behavior. We carry out extensive experiments on large-scale commercial datasets and demonstrate the effectiveness of the proposed methods.

Recent advances in click models have positioned them as an effective approach to the improvement of interpreting click data, and some typical works include UBM, DBN, CCM, etc. After formulating the knowledge of user search behavior into a set of model assumptions, each click model developed an inference method to estimate its parameters. The inference method plays a critical role in terms of accuracy in interpreting clicks, and we observe that different inference methods for a click model can lead to significant accuracy differences. In this paper, we propose a novel Bayesian inference approach for click models. This approach regards click model under a unified framework, which has the following characteristics and advantages:

Much work has attempted to model a user’s click-through behavior by mining the click logs. The task is not trivial due to the well-known position bias problem. Some breakthroughs have been made: two newly proposed click models, DBN and CCM, addressed this problem and improved document relevance estimation. However, to further improve the estimation, we need a model that can capture more sophisticated user behaviors. In particular, after clicking a search result, a user’s behavior (such as the dwell time on the clicked document, and whether there are further clicks on the clicked document) can be highly indicative of the relevance of the document. Unfortunately, such measures have not been incorporated in previous click models. In this paper, we introduce a novel click model, called the post-click click model (PCC), which provides an unbiased estimation of document relevance through leveraging both click behaviors on the search page and post-click behaviors beyond the search page. The PCC model is based on the Bayesian approach, and because of its incremental nature, it is highly scalable to large scale and constantly growing log data. Extensive experimental results illustrate that the proposed method significantly outperforms the state of the art methods merely relying on click logs. 1.

Several relevance metrics, such as NDCG, precision and pSkip, are proposed to measure search relevance, where different metrics try to characterize search relevance from different perspectives. Yet we empirically find that the direct optimization of one metric cannot always achieve the optimal ranking of another metric. In this paper, we propose two novel relevance optimization approaches, which take different metrics into a global consideration where the objective is to achieve an ideal tradeoff between different metrics. To achieve this objective, we propose to co-optimize multiple relevance metrics and show their effectiveness.

Traditional boosting algorithms for the ranking problems usually employ the pairwise approach and convert the document rating preference into a binary-value label, like RankBoost. However, such a pairwise approach ignores the information about the magnitude of preference in the learning process. In this paper, we present the directed distance function (DDF) as a substitute for binary labels in pairwise approach to preserve the magnitude of preference and propose a new boosting algorithm called MPBoost, which applies GentleBoost optimization and directly incorporates DDF into the exponential loss function. We give the boundedness property of MPBoost through theoretic analysis. Experimental results demonstrate that MPBoost not only leads to better NDCG accuracy as compared to state-of-the-art ranking solutions in both public and commercial datasets, but also has good properties of avoiding the overfitting problem in the task of learning ranking functions.