Yindu Su, Huike Zou, Lin Sun, Ting Zhang, Haiyang Yang, Chen Li Yu, David Lo, Qingheng Zhang, Shuguang Han, Jufeng Chen. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Most recommendation systems typically follow a product-based paradigm utilizing user-product interactions to identify the most engaging items for users. However, this product-based paradigm has notable drawbacks for Xianyu~. Xianyu is China's largest online C2C e-commerce platform where a large portion of the product are post by individual sellers. Most of the product on Xianyu posted from individual sellers often have limited stock available for distribution, and once the product is sold, it's no longer available for distribution. This result in most items distributed product on Xianyu having relatively few interactions, affecting the effectiveness of traditional recommendation depending on accumulating user-item interactions. To address these issues, we introduce IU4Rec, an Interest Unit-based two-stage Recommendation system framework. We first group products into clusters based on attributes such as category, image, and semantics. These IUs are then integrated into the Recommendation system, delivering both product and technological innovations. IU4Rec begins by grouping products into clusters based on attributes such as category, image, and semantics, forming Interest Units (IUs). Then we redesign the recommendation process into two stages. In the first stage, the focus is on recommend these Interest Units, capturing broad-level interests. In the second stage, it guides users to find the best option among similar products within the selected Interest Unit. User-IU interactions are incorporated into our ranking models, offering the advantage of more persistent IU behaviors compared to item-specific interactions. This interest unit based recommendation can be beneficial from mitigating the side effect of limited-stock problem, since most interaction can be gathered on interest units which can persist and accumulate over time. Experimental results on the production dataset and online A/B testing demonstrate the effectiveness and superiority of our proposed IU-centric recommendation approach. This study not only advances recommendation technologies but also emphasizes the potential for co-evolution between product innovations and the technologies involved in item supply and distribution.

In the real world, the data collected naturally are often long-tailed, which inevitably leads to class-imbalanced prediction and performance degradation. As a simple and effective data augmentation method, Mixup has been proven to be beneficial for the tail class in recent long-tail learning studies. However, samples selected by Mixup are still imbalanced, which could exacerbate the imbalance problem. Existing work always considers adjusting the input distribution to alleviate this problem, while this may lead to over-fitting in the tail class. In this paper, we detail the theoretical analysis of the data imbalance caused by Mixup, and propose a novel Balanced Mixup (BaMix) loss function from the output perspective. By adding a balance term, we theoretically prove the BaMix loss can overcome the imbalance caused by Mixup. In the experiments, our solution achieves the state-of-the-art performance on CIFAR-LT, ImageNet-LT, and iNaturalist 2018.

Long-tailed data distribution (i.e., minority classes occupy most of the data, while most classes have very few samples) is a common problem in image classification. The existing deferred re-balancing methods suffer from low accuracy for tail classes due to their bias towards head classes. In this paper, we find that properly adjusting the momentum can significantly improve the performance for class-imbalanced tasks, which provides a novel perspective to solve this thorny problem. Based on this finding, we propose a new way to calculate a distribution-aware momentum (DAM) based on the data distribution to avoid bias towards head classes. Furthermore, we design a multi-expert network (MEN) to designate each expert to focus on different tail classes. We achieve new state-of-the-art on CIFAR-10-LT, CIFAR-100-LT, ImageNet-LT and iNaturalist2018 for image classification.

Effective visual-semantic representation is critical to the image-text matching task. Various methods are proposed to develop image representation with more semantic concepts and a lot of progress has been achieved. However, the internal hierarchical structure in both image and text, which could effectively enhance the semantic representation, is rarely explored in the image-text matching task. In this work, we propose a Hierarchical Visual-Semantic Network (HVSN) with fine-grained semantic alignment to exploit the hierarchical structure. Specifically, we first model the spatial or semantic relationship between objects and aggregate them into visual semantic concepts by the Local Relational Attention (LRA) module. Then we employ Gated Recurrent Unit (GRU) to learn relationships between visual semantic concepts and generate the global image representation. For the text part, we develop phrase features from related words, then generate text representation by learning relationships between these phrases. Besides, the model is trained with joint optimization of image-text retrieval and phrase alignment task to capture the fine-grained interplay between vision and language. Our approach achieves state-of-the-art performance on Flickr30K and MS-COCO datasets. On Flickr30K, our approach outperforms the current state-of-the-art method by 3.9% relatively in text retrieval with image query and 1.3% relatively for image retrieval with text query (based on [email protected]). On MS-COCO, our HVSN improves image retrieval by 2.3% relatively and text retrieval by 1.2% relatively. Both quantitative and visual ablation studies are provided to verify the effectiveness of the proposed modules.

In the active research area of employing embedding models for knowledge graph completion, particularly for the task of link prediction, most prior studies used two benchmark datasets FB15k and WN18 in evaluating such models. Most triples in these and other datasets in such studies belong to reverse and duplicate relations which exhibit high data redundancy due to semantic duplication, correlation or data incompleteness. This is a case of excessive data leakage---a model is trained using features that otherwise would not be available when the model needs to be applied for real prediction. There are also Cartesian product relations for which every triple formed by the Cartesian product of applicable subjects and objects is a true fact. Link prediction on the aforementioned relations is easy and can be achieved with even better accuracy using straightforward rules instead of sophisticated embedding models. A more fundamental defect of these models is that the link prediction scenario, given such data, is non-existent in the real-world. This paper is the first systematic study with the main objective of assessing the true effectiveness of embedding models when the unrealistic triples are removed. Our experiment results show these models are much less accurate than what we used to perceive. Their poor accuracy renders link prediction a task without truly effective automated solution. Hence, we call for re-investigation of possible effective approaches.

We study the problem of embedding-based entity alignment between knowledge graphs (KGs). Previous works mainly focus on the relational structure of entities. Some further incorporate another type of features, such as attributes, for refinement. However, a vast of entity features are still unexplored or not equally treated together, which impairs the accuracy and robustness of embedding-based entity alignment. In this paper, we propose a novel framework that unifies multiple views of entities to learn embeddings for entity alignment. Specifically, we embed entities based on the views of entity names, relations and attributes, with several combination strategies. Furthermore, we design some cross-KG inference methods to enhance the alignment between two KGs. Our experiments on real-world datasets show that the proposed framework significantly outperforms the state-of-the-art embedding-based entity alignment methods. The selected views, cross-KG inference and combination strategies all contribute to the performance improvement.

Embedding-based entity alignment represents different knowledge graphs (KGs) as low-dimensional embeddings and finds entity alignment by measuring the similarities between entity embeddings. Existing approaches have achieved promising results, however, they are still challenged by the lack of enough prior alignment as labeled training data. In this paper, we propose a bootstrapping approach to embedding-based entity alignment. It iteratively labels likely entity alignment as training data for learning alignment-oriented KG embeddings. Furthermore, it employs an alignment editing method to reduce error accumulation during iterations. Our experiments on real-world datasets showed that the proposed approach significantly outperformed the state-of-the-art embedding-based ones for entity alignment. The proposed alignment-oriented KG embedding, bootstrapping process and alignment editing method all contributed to the performance improvement.

The conventional range gated lidar systems require long laser pulse width and range bins to maintain the far detection range, which limits the ranging resolution of the system. A range gated lidar system with phase coded method is proposed with high ranging resolution. The system utilizes a kN+1 bits of the laser pulse sequence and controls the accumulation of the detector in the range bins to get the echo signals. The correlation calculation of the accumulated signals is conducted in the computer to acquire the targe range with the ranging resolution defined by the sequence bit width. The indoor demonstration experiment achieved 2 us resolution with the sampling period of 32 us by employing a 15-bit m-sequence. The result shows the ability of achieving high ranging resolution of range gated lidar.