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.

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.

Modeling user-item interactions in a dynamic manner bring new insight to the representation learning for recom-mender systems. Distinct from static graph-based approaches that model the whole user-item interaction graph, dynamic graph-based approaches model both the structural and temporal information from a sequence of snapshot graphs. Despite effectiveness, we argue that existing approaches do not explicitly address the temporal sparsity issue, which degrades the representation learning performance for inactive users and items. Therefore, we propose a new Dynamic Graph-based Collaborative Filtering(DynGCF) framework. In particular, it utilizes the vanilla interaction graph with the co-occurrence graph(co-graph) to jointly explores 1-hop collaborative and 2-hop implicit similarity for dynamic representation learning. Moreover, to further alleviate temporal sparsity, we explore representative(active) users and items via graph pooling and design an activity-guided gating(AGate) layer to augment inactive users and items. At last, we further stack a temporal aggregator layer to obtain the final representation. We conduct extensive experiments on four real-world benchmark datasets to demonstrate the significant performance gains for DynGCF over several state-of-the-art methods. Further analyses also show the necessity of alleviating temporal sparsity for improving recommendation performance.

Recently, sequence features have been extensively studied to improve the performance of recommender systems. However, advanced sequential recommendation methods that rely only on item IDs still face the challenge of modeling fine-grained user preference from interactive data. Furthermore, context-aware sequential recommendations have the hardness of modeling the relationship between items and items, items and users. Both of these two methods ignore the effect of categories on users' next click tendency and the interactive learning between categories and items. In this paper, we propose a method named Contextual Collaborative Graph Attention Network (CCGAT) to model the sequence. Methodologically, user behavior sequences are constructed as graph-structured data, and we apply two similar graph self-attention networks to model the item transitions and the category click probability. CCGAT takes advantage of the fact that users tend to click on the same or similar categories under specific purposes, and provides a simple but effective way to train two networks collaboratively. Extensive experiments on five real-world datasets show that our model outperforms state-of-the-art methods, and demonstrate the validity of modeling both contextual information and graph features.

Session-based recommendation aims to predict the next item that users will interact based solely on anonymous sessions. In real-life scenarios, the user's preferences are usually various, and distinguishing different preferences in the session is important. However previous studies focus mostly on the transition modeling between items, ignoring the mining of various user preferences. In this paper, we propose a Hierarchical Leaping Network (HLN) to explicitly model the users' multiple preferences by grouping items that share some relationships. We first design a Leap Recurrent Unit (LRU) which is capable of skipping preference-unrelated items and accepting knowledge of previously learned preferences. Then we introduce a Preference Manager (PM) to manage those learned preferences and produce an aggregated preference representation each time LRU reruns. The final output of PM which contains multiple preferences of the user is used to make recommendations. Experiments on two benchmark datasets demonstrate the effectiveness of HLN. Furthermore, the visualization of explicitly learned subsequences also confirms our idea.

Over recent years, massive geospatial information has been produced at a prodigious rate, and is usually geographically distributed across the Internet. Grid computing, as a recent development in the landscape of distributed computing, is deemed as a good solution for distributed geospatial data management and manipulation. Thus, the Grid computing technology can be applied to integrate various distributed resources into a ‘super-computer’ that enables efficient distributed geospatial query processing. In order to realize this vision, an effective mechanism for building the distributed geospatial query workflow in the Grid environment needs to be elaborately designed. The workflow-building technology aims to automatically transform the global geospatial query into an equivalent distributed query process in the Grid. In response to this goal, detailed steps and algorithms for building the distributed geospatial query workflow in the Grid environment are discussed in this article. Moreover, we develop corresponding software tools that enable Grid-based geospatial queries to be run against multiple data resources. Experimental results demonstrate that the proposed methodology is feasible and correct.

As increasingly large‐scale and higher‐resolution terrain data have become available, for example air‐form and space‐borne sensors, the volume of these datasets reveals scalability problems with existing GIS algorithms. To address this problem, a kind of serial algorithm was developed to generate viewshed on large grid‐based digital elevation models (DEMs). We first divided the whole DEM into rectangular blocks in row and column directions (called block partitioning), then processed these blocks with four axes followed by four sectors sequentially. When processing the particular block, we adopted the ‘reference plane’ algorithm to calculate the visibility of the target point on the block, and adjusted the calculation sequence according to the different spatial relationships between the block and the viewpoint since the viewpoint is not always inside the DEM. By adopting the ‘Reference Plane’ algorithm and using a block partitioning method to segment and load the DEM dynamically, it is possible to generate viewshed efficiently in PC‐based environments. Experiments showed that the divided block should be dynamically loaded whole into computer main memory when partitioning, and the suggested approach retains the accuracy of the reference plane algorithm and has near linear compute complexity.