Deep learning models, usually trained on large datasets, have witnessed great achievements for both classification and regression tasks during past years. However, in practical settings, the datasets are commonly imbalanced where the number of data samples differs among labels (i.e.,categories or target values), leading to serious performance degradation of the trained models. Although many prior works have been proposed to solve the data imbalance problem for the classification task, there are still limited works considering the regression task. In this work, we identify the distinct challenges in regression problems compared to traditional classification problems, such as managing the continuous nature of the target variable and navigating fuzzy decision-making boundaries. To address these challenges, we propose an Adaptive Weighted Statistical Space Smoothing method (AWSSS), which alleviates the impact of imbalanced data by learning from continuously valued, imbalanced data and extending local similarity across the entire target range. AWSSS extracts statistical values from the original dataset and smoothes statistical features. Subsequently, it aligns features across different regions and applies adaptive weights to facilitate knowledge transfer between adjacent areas. Finally, AWSSS calculates the loss values, which are used to refine the model’s parameters through iterative updates. Extensive experiments conducted on various datasets demonstrate the effectiveness of the proposed method as compared to state-of-the-art methods.

Learning feature interactions is crucial for model performance in online recommendations. Extensive studies are devoted to designing effective structures for learning interactive information in an explicit way and tangible progress has been made. However, the core interaction calculations of these models are artificially specified, such as inner product, outer product and self-attention, which results in high dependence on domain knowledge. Hence model effect is bounded by both restriction of human experience and the finiteness of candidate operations. In this paper, we propose a generalized interaction paradigm to lift the limitation, where operations adopted by existing models can be regarded as its special form. Based on this paradigm, we design a novel model to adaptively explore and optimize the operation itself according to data, named generalized interaction network(GIN). We proved that GIN is a generalized form of a wide range of state-of-the-art models, which means GIN can automatically search for the best operation among these models as well as a broader underlying architecture space. Finally, an architecture adaptation method is introduced to further boost the performance of GIN by discriminating important interactions. Thereby, architecture and operation adaptive network(AOANet) is presented. Experiment results on two large scale datasets show the superiority of our model. AOANet has been deployed to industrial production. In a 7-day A/B test, the click-through rate increased by 10.94%, which represents considerable business benefits.

Recently, network embedding has been successfully used in recommendation systems. Researchers have made efforts to utilize additional auxiliary information (e.g., social relations of users) to improve performance. However, such auxiliary information lacks compatibility for all recommendation scenarios, thus it is difficult to apply in some industrial scenarios where generality is required. Moreover, the heterogeneous nature between users and items aggravates the difficulty in network information fusion. Many works tried to transform user-item heterogeneous network to two homogeneous graphs (i.e., user-user and item-item), and then fuse information separately. This may limit the representation power of learned embedding due to ignoring the adjacent relationship in the original graph. In addition, the sparsity of user-item interactions is an urgent problem need to be solved. To solve the above problems, we propose a universal and effective framework named Gemini, which only relies on the common interaction logs, avoiding the dependence on auxiliary information and ensuring a better generality. For the purpose of keeping original adjacent relationship, Gemini transforms the original user-item heterogeneous graph into two semi homogeneous graphs from the perspective of users and items respectively. The transformed graphs consist of two types of nodes: network nodes coming from homogeneous nodes and attribute nodes coming from heterogeneous node. Then, the node representation is learned in a homogeneous way, with considering edge embedding at the same time. Simultaneously, the interaction sparsity problem is solved to some extent as the transformed graphs contain the original second-order neighbors. For training efficiently, we also propose an iterative training algorithm to reduce computational complexity. Experimental results on the five datasets and online A/B tests in recommendations of DiDiChuXing show that Gemini outperforms state-of-the-art algorithms.

Point-of-interest (POI) recommendation is one of the most important services in the rapid growing location-based social networks (LBSNs). Good POI recommendation can help people explore the locations they haven't visited but are interested in, and help merchants find their target users. Time-aware POI recommendation aims to recommend unvisited POIs for a given user at a specified time in a day. However, previous methods, such as user-based collaborative filtering, lack the mining of the features of POIs and the learning of abstract spatio-temporal interactions. In this paper, we propose a novel time-aware POI recommendation method named ST-RNet (Spatio-Temporal Recommender Network) to address these shortages. ST-RNet works in the following fashion. Firstly, we analyze the crucial features in LBSNs to alleviate data sparsity problem and further measure the similarities between POIs. For subsequent network training, we then construct the embedding matrices with same dimension for users and POIs by POI-based Collaborative Filtering (PCF). Furthermore, the positive and negative check-in records are fed into a novel recommender neural network (RNet) to learn the embedding matrix of times and the abstract interactions between users, POIs and times. Finally, ST-RNet recommends the unvisited POIs most likely to be visited to a given user at a given time. The experimental results on Foursquare real-world dataset show that ST-RNet is effective on time-aware POI recommendation task and is capable of analyzing the hidden patterns behind spatio-temporal interactions.

Predicting users' irregular trips in a short term period is one of the crucial tasks in the intelligent transportation system. With the prediction, the taxi requesting services, such as Didi Chuxing in China, can manage the transportation resources to offer better services. There are several different transportation scenes, such as commuting scene and entertainment scene. The origin and the destination of entertainment scene are more unsure than that of commuting scene, so both origin and destination should be predicted. Moreover, users' trips on Didi platform is only a part of their real life, so these transportation data are only few weak samples. To address these challenges, in this paper, we propose Taxi Demand Prediction (TDP) model in challenging entertainment scene based on heterogeneous graph embedding and deep neural predicting network. TDP aims to predict next possible trip edges that have not appeared in historical data for each user in entertainment scene. Experimental results on the real-world dataset show that TDP achieves significant improvements over the state-of-the-art methods.