Reviewerly/exHarmony huggingface dataset:
Few-Shot Learning via Task-Aware Discriminant Local Descriptors Network

Few-shot learning for image classification task aims to classify images from several novel classes with limited number of samples. Recent studies have shown that the deep local descriptors have better representation ability than image-level features, and achieve great success. However, most of these methods often use all local descriptors or over-screening local descriptors for classification. The former contains some task-irrelevant descriptors, which may misguide the final classification result. The latter is likely to lose some key descriptors. In this paper, we propose a novel Task-Aware Discriminant local descriptors Network (TADNet) to address these issues, which can adaptively select the discriminative query descriptors and eliminate the task-irrelevant query descriptors among the entire task. Specifically, TADNet assigns a value to each query descriptor by comparing its similarity to all support classes to represent its discriminant power for classification. Then the discriminative query descriptors can be preserved via a task-aware attention map. Extensive experiments on both fine-grained and generalized datasets demonstrate that the proposed TADNet outperforms the existing state-of-the-art methods.
Knowledge-Aware Cross-Semantic Alignment for Domain-Level Zero-Shot Recommendation

Recommendation systems have attracted attention from academia and industry due to their wide range of application scenarios. However, cold start remains a challenging problem limited by sparse user interactions. Some scholars propose to transfer the dense information from the source domain to the target domain through cross-domain recommendation, but most of the work assumes that there is a small amount of historical interaction in the target domain. However, this approach essentially presupposes the existence of at least some historical interaction within the target domain. In this paper, we focus on the domain-level zero-shot recommendation (DZSR) problem. To address the above challenges, we propose a knowledge-aware cross-semantic alignment (K-CSA) framework to learn transferable source domain semantic information. The motivation is to establish stable alignments of interests in different domains through class semantic descriptions (CSDs). Specifically, due to the lack of effective information in the target domain, we learn semantic representations of source and target domain items based on knowledge graphs. Moreover, we conduct multi-view K-means to extract item CSDs from the learned semantic representations. Further, K-CSA learns universal user CSDs through the designed multi-head self-attention. To facilitate the transference of user interest from the source domain to the target domain, we devise a cross-semantic contrastive learning strategy, grounded in the prototype distribution matrix. We conduct extensive experiments on several real-world cross-domain datasets, and the experimental results clearly demonstrate the superiority of our proposed K-CSA compared with other baselines.
TCCM: Time and Content-Aware Causal Model for Unbiased News Recommendation

Popularity bias significantly impacts news recommendation systems, as popular news articles receive more exposure and are often delivered to irrelevant users, resulting in unsatisfactory performance. Existing methods have not adequately addressed the issue of popularity bias in news recommendations, largely due to the neglect of the time factor and the impact of news content on popularity. In this paper, we propose a novel approach called Time and Content-aware Causal Model, namely TCCM. It models the effects of three factors on user interaction behavior, i.e., the time factor, the news popularity, and the matching between news content and user interest. TCCM also estimates news popularity more accurately by incorporating the news content, i.e., the popularity of entity and words. Causal intervention techniques are applied to obtain debiased recommendations. Extensive experiments on well-known benchmark datasets demonstrate that the proposed approach outperforms a range of state-of-the-art techniques.
Continuous Personalized Knowledge Tracing: Modeling Long-Term Learning in Online Environments

With the advance of online education systems, accessibility to learning materials has increased. In these systems, students can practice independently and learn from different learning materials over long periods of time. As a result, it is essential to trace students' knowledge states over long learning sequences while maintaining a personalized model of each individual student's progress. However, the existing deep learning-based knowledge tracing models are either not personalized or not tailored for handling long sequences. Handling long sequences are especially essential in the online education environments, in where models are preferred to be updated with the newly collected user data in a timely manner as students could acquire knowledge on each learning activity. In this paper, we propose a knowledge tracing model, Continuous Personalized Knowledge Tracing (CPKT), that can mimic the real-world long-term continuous learning scenario by incorporating a novel online model training paradigm that is suitable for the knowledge tracing problem. To achieve personalized knowledge tracing, we propose two model components: 1) personalized memory slots to maintain learner's knowledge in a lifelong manner, and 2) personalized user embeddings that help to accurately predict the individual responses, correctly detect the personalized knowledge acquisition and forgetting patterns, and better interpret and analyze the learner's progress. Additionally, we propose transition-aware stochastic shared embedding according to the learning transition matrix to regularize the online model training. Extensive experiments on four real-world datasets showcase the effectiveness and superiority of CPKT, especially for students with longer sequences.
Mulco: Recognizing Chinese Nested Named Entities through Multiple Scopes

Nested Named Entity Recognition (NNER), as a subarea of Named Entity Recognition, has presented longstanding challenges to researchers. In NNER, one entity may be part of a larger entity, which can occur at multiple levels. These nested structures prevent traditional sequence labeling methods from properly recognizing all entities. While recent research has focused on designing better recognition methods for NNER in various languages, Chinese Nested Named Entity Recognition (CNNER) is still underdeveloped, largely due to a lack of freely available CNNER benchmarks. To support CNNER research, in this paper, we introduce ChiNesE, a CNNER dataset comprising 20,000 sentences from online passages in multiple domains and containing 117,284 entities that fall into 10 categories, of which 43.8% are nested named entities. Based on ChiNesE, we propose Mulco, a novel method that can recognize named entities in nested structures through multiple scopes. Each scope uses a scope-based sequence labeling method that predicts an anchor and the length of a named entity to recognize it. Experimental results show that Mulco outperforms state-of-the-art baseline methods with different recognition schemes on ChiNesE and ACE 2005 Chinese corpus.
Cracking the Code of Negative Transfer: A Cooperative Game Theoretic Approach for Cross-Domain Sequential Recommendation

This paper investigates Cross-Domain Sequential Recommendation (CDSR), a promising method that uses information from multiple domains (more than three) to generate accurate and diverse recommendations, and takes into account the sequential nature of user interactions. The effectiveness of these systems often depends on the complex interplay among the multiple domains. In this dynamic landscape, the problem of negative transfer arises, where heterogeneous knowledge between dissimilar domains leads to performance degradation due to differences in user preferences across these domains. As a remedy, we propose a new CDSR framework that addresses the problem of negative transfer by assessing the extent of negative transfer from one domain to another and adaptively assigning low weight values to the corresponding prediction losses. To this end, the amount of negative transfer is estimated by measuring the marginal contribution of each domain to model performance based on a cooperative game theory. In addition, a hierarchical contrastive learning approach that incorporates information from the sequence of coarse-level categories into that of fine-level categories (e.g., item level) when implementing contrastive learning was developed to mitigate negative transfer. Despite the potentially low relevance between domains at the fine-level, there may be higher relevance at the category level due to its generalised and broader preferences. We show that our model is superior to prior works in terms of model performance on two real-world datasets across ten different domains.
Retrieval-Based Unsupervised Noisy Label Detection on Text Data

The success of deep neural networks hinges on both high-quality annotations and copious amounts of data; however, in practice, a compromise between dataset size and quality frequently arises. Data collection and cleansing are often resource-intensive and time-consuming, leading to real-world datasets containing label noise that can introduce incorrect correlation patterns, adversely affecting model generalization capabilities. The efficient identification of corrupted patterns is indispensable, with prevalent methods predominantly concentrating on devising robust training techniques to preclude models from internalizing these patterns. Nevertheless, these supervised approaches often necessitate tailored training procedures, potentially resulting in overfitting corrupted patterns and a decline in detection performance. This paper presents a retrieval-based unsupervised solution for the detection of noisy labels, surpassing the performance of three current competitive methods in this domain.
AQUAPLANE: The Argument Quality Explainer App

In computational argumentation, so-called quality dimensions such as coherence or rhetoric are often used for ranking arguments. However, the literature often only predicts which argument is more persuasive, but not why this is the case. In this paper, we introduce AQUAPLANE, a transparent and easy-to-extend application that not only decides for a pair of arguments which one is more convincing with respect to a statement, but also provides an explanation.
Depth-based Sampling and Steering Constraints for Memoryless Local Planners

Abstract By utilizing only depth information, the paper introduces a novel two-stage planning approach that enhances computational efficiency and planning performances for memoryless local planners. First, a depth-based sampling technique is proposed to identify and eliminate a specific type of in-collision trajectories among sampled candidates. Specifically, all trajectories that have obscured endpoints are found through querying the depth values and will then be excluded from the sampled set, which can significantly reduce the computational workload required in collision checking. Subsequently, we apply a tailored local planning algorithm that employs a direction cost function and a depth-based steering mechanism to prevent the robot from being trapped in local minima. Our planning algorithm is theoretically proven to be complete in convex obstacle scenarios. To validate the effectiveness of our DEpth-based both Sampling and Steering (DESS) approaches, we conducted experiments in simulated environments where a quadrotor flew through cluttered regions with multiple various-sized obstacles. The experimental results show that DESS significantly reduces computation time in local planning compared to the uniform sampling method, resulting in the planned trajectory with a lower minimized cost. More importantly, our success rates for navigation to different destinations in testing scenarios are improved considerably compared to the fixed-yawing approach.
A Generalized Propensity Learning Framework for Unbiased Post-Click Conversion Rate Estimation

This paper addresses the critical gap in the unbiased estimation of post-click conversion rate (CVR) in recommender systems. Existing CVR prediction methods, such as Inverse Propensity Score (IPS) and various Doubly Robust (DR) based estimators, overlook the impact of propensity estimation on the model bias and variance, thus leading to a debiasing performance gap. We propose a Generalized Propensity Learning (GPL) framework to directly minimize the bias and variance in CVR prediction models. The proposed method works as a complement to existing methods like IPS, DR, MRDR, and DRMSE to improve prediction performance by reducing their bias and variance. Extensive experiments on real-world datasets and semi-synthetic datasets demonstrate the significant performance promotion brought by our proposed method. Data and code can be found at: https://github.com/yuqing-zhou/GPL.
Unlocking the Potential of User Feedback: Leveraging Large Language Model as User Simulators to Enhance Dialogue System

Dialogue systems and large language models (LLMs) have gained considerable attention. However, the direct utilization of LLMs as task-oriented dialogue (TOD) models has been found to underperform compared to smaller task-specific models. Nonetheless, it is crucial to acknowledge the significant potential of LLMs and explore improved approaches for leveraging their impressive abilities. Motivated by the goal of leveraging LLMs, we propose an alternative approach called User-Guided Response Optimization (UGRO) to combine it with a smaller TOD model. This approach uses LLM as an annotation-free user simulator to assess dialogue responses, combining them with smaller fine-tuned end-to-end TOD models. By utilizing the satisfaction feedback generated by LLMs, UGRO further optimizes the supervised fine-tuned TOD model. Specifically, the TOD model takes the dialogue history as input and, with the assistance of the user simulator's feedback, generates high-satisfaction responses that meet the user's requirements. Through empirical experiments on two TOD benchmarks, we validate the effectiveness of our method. The results demonstrate that our approach outperforms previous state-of-the-art (SOTA) results.
Learning What to Ask: Mining Product Attributes for E-commerce Sales from Massive Dialogue Corpora

Conversational Recommender Systems (CRSs) are extensively applied in e-commercial platforms that recommend items to users. To ensure accurate recommendation, agents usually ask for users' preferences towards specific product attributes which are pre-defined by humans. In e-commercial platforms, however, the number of products easily reaches to billions, making it prohibitive to pre-define decisive attributes for efficient recommendation due to the lack of substantial human resources and the scarce domain expertise. In this work, we present AliMeMOSAIC, a novel knowledge mining and conversational assistance framework that extracts core product attributes from massive dialogue corpora for better conversational recommendation experience. It first extracts user-agent interaction utterances from massive corpora that contain product attributes. A Joint Attribute and Value Extraction (JAVE) network is designed to extract product attributes from user-agent interaction utterances. Finally, AliMeMOSAIC generates attribute sets that frequently appear in dialogues as the target attributes for agents to request, and serve as an assistant to guide the dialogue flow. To prove the effectiveness of AliMeMOSAIC, we show that it consistently improves the overall recommendation performance of our CRS system. An industrial demonstration scenario is further presented to show how it benefits online shopping experiences.
Efficient Exact Minimum k-Core Search in Real-World Graphs

The k-core, which refers to the induced subgraph with a minimum degree of at least k, is widely used in cohesive subgraph discovery and has various applications. However, the k-core in real-world graphs tends to be extremely large, which hinders its effectiveness in practical applications. This challenge has motivated researchers to explore a variant of the k-core problem known as the minimum k-core search problem. This problem has been proven to be NP-Hard, and most of the existing studies naturally either deal with approximate solutions or suffer from inefficiency in practice. In this paper, we focus on designing efficient exact algorithms for the minimum k-core search problem. In particular, we develop an iterative-based framework that decomposes an instance of the minimum k-core search problem into a list of problem instances on another well-structured graph pattern. Based on this framework, we propose an iterative-based branch-and-bound algorithm, namely IBB, with additional pruning and reduction techniques. We show that, with a n-vertex graph, IBB runs in cn nO(1) time for some c < 2, achieving better theoretical performance than the trivial bound of 2n nO(1). Finally, our experiments on real-world graphs demonstrate that IBB is up to three orders of magnitude faster than the state-of-the-art algorithms on real-world datasets.
Accelerating Concept Learning via Sampling

Node classification is an important task in many fields, e.g., predicting entity types in knowledge graphs, classifying papers in citation graphs, or classifying nodes in social networks. In many cases, it is crucial to explain why certain predictions are made. Towards this end, concept learning has been proposed as a means of interpretable node classification: given positive and negative examples in a knowledge base, concepts in description logics are learned that serve as classification models. However, state-of-the-art concept learners, including EvoLearner and CELOE exhibit long runtimes. In this paper, we propose to accelerate concept learning with graph sampling techniques. We experiment with seven techniques and tailor them to the setting of concept learning. In our experiments, we achieve a reduction in training size by over 90% while maintaining a high predictive performance.
Rec4Ad: A Free Lunch to Mitigate Sample Selection Bias for Ads CTR Prediction in Taobao

Click-Through Rate (CTR) prediction serves as a fundamental component in online advertising. A common practice is to train a CTR model on advertisement (ad) impressions with user feedback. Since ad impressions are purposely selected by the model itself, their distribution differs from the inference distribution and thus exhibits sample selection bias (SSB) that affects model performance. Existing studies on SSB mainly employ sample re-weighting techniques which suffer from high variance and poor model calibration. Another line of work relies on costly uniform data that is inadequate to train industrial models. Thus mitigating SSB in industrial models with a uniform-data-free framework is worth exploring. Fortunately, many platforms display mixed results of organic items (i.e., recommendations) and sponsored items (i.e., ads) to users, where impressions of ads and recommendations are selected by different systems but share the same user decision rationales. Based on the above characteristics, we propose to leverage recommendations samples as a free lunch to mitigate SSB for ads CTR model (Rec4Ad). After elaborating data augmentation, Rec4Ad learns disentangled representations with alignment and decorrelation modules for enhancement. When deployed in Taobao display advertising system, Rec4Ad achieves substantial gains in key business metrics, with a lift of up to +6.6% CTR and +2.9% RPM.
Datasets and Interfaces for Benchmarking Heterogeneous Graph Neural Networks

In recent years, Heterogeneous Graph Neural Networks (HGNNs) have gained increasing attention due to their excellent performance in applications. However, the lack of high-quality benchmarks in new fields has become a critical limitation for developing and applying HGNNs. To accommodate the urgent need for emerging fields and the advancement of HGNNs, we present two large-scale, real-world, and challenging heterogeneous graph datasets from real scenarios: risk commodity detection and takeout recommendation. Meanwhile, we establish standard benchmark interfaces that provide over 40 heterogeneous graph datasets. We provide initial data split, unified evaluation metrics, and baseline results for future work, making it fair and handy to explore state-of-the-art HGNNs. Our interfaces also offer a comprehensive toolkit to research the characteristics of graph datasets. The above new datasets are publicly available on https://zenodo.org/communities/hgd, and the interface codes are available at https://github.com/BUPT-GAMMA/hgbi.
Predicting Interaction Quality of Conversational Assistants With Spoken Language Understanding Model Confidences

In conversational AI assistants, SLU models are part of a complex pipeline composed of several modules working in harmony. Hence, an update to the SLU model needs to ensure improvements not only in the model specific metrics but also in the overall conversational assistant performance. Specifically, the impact on user interaction quality metrics must be factored in, while integrating interactions with distal modules upstream and downstream of the SLU component. We develop a ML model that makes it possible to gauge the interaction quality metrics due to SLU model changes before a production launch. The proposed model is a multi-modal transformer with a gated mechanism that conditions on text embeddings, output of a BERT model pre-trained on conversational data, and the hypotheses of the SLU classifiers with the corresponding confidence scores. We show that the proposed model predicts defect with more than 76% correlation with live interaction quality defects, compared to 46% baseline.
Dual-Process Graph Neural Network for Diversified Recommendation

The recommender system is one of the most fundamental information services. A significant effort has been devoted to improving prediction accuracy, inevitably leading to the potential degradation of recommendation diversity. Moreover, individuals have different needs for diversity. To address these problems, diversity-enhanced approaches are proposed to modify the recommender models. However, these methods fail to break free from the relevance-oriented paradigm and are mostly haunted by sharply-declined accuracy and high computational costs. To tackle these challenges, we propose the Dual-Process Graph Neural Network (DPGNN), an efficient diversity-enhanced recommender system, resonating with the dual-process model of human cognition and the arousal theory of human interest. The first stage reduces the risk of suboptimal output during the training procedure, which helps to find a solution outside the relevance-oriented paradigm. Moreover, the second stage utilizes user-specific rating adjustments, boosting the recommendation diversity and accommodating users' distinctive needs with minimum computational costs. Extensive experiments on real-world datasets verify the effectiveness of our method in improving diversity, while maintaining accuracy with low computational costs.