OpenRaiser/Intern-Atlas huggingface dataset:
Safe-NORA: Safe Reinforcement Learning-based Mobile Network Resource Allocation for Diverse User Demands
Authors: Wenzhen Huang, Tong Li, Yuting Cao, Zhe Lyu, Yanping Liang, Li Yu, Depeng Jin, Junge Zhang, Yong Li
Venue: CIKM
Year: 2023

As mobile communication technologies advance, mobile networks become increasingly complex, and user requirements become increasingly diverse. To satisfy the diverse demands of users while improving the overall performance of the network system, the limited wireless network resources should be efficiently and dynamically allocated to them based on the magnitude of their demands and their relative location to the base stations. We separated the problem into four constrained subproblems, which we then solved using a safe reinforcement learning method. In addition, we design a reward mechanism to encourage agent cooperation in distributed training environments. We test our methodology in a simulated scenario with thousands of users and hundreds of base stations. According to experimental findings, our method guarantees that over 95% of user demands are satisfied while also maximizing the overall system throughput.
Spans, Not Tokens: A Span-Centric Model for Multi-Span Reading Comprehension
Authors: Zixian Huang, Jiaying Zhou, Chenxu Niu, Gong Cheng
Venue: CIKM
Year: 2023

Many questions should be answered by not a single answer but a set of multiple answers. This emerging Multi-Span Reading Comprehension (MSRC) task requires extracting multiple non-contiguous spans from a given context to answer a question. Existing methods extend conventional single-span models to predict the positions of the start and end tokens of answer spans, or predict the beginning-inside-outside tag of each token. Such token-centric paradigms can hardly capture dependencies among span-level answers which are critical to MSRC. In this paper, we propose SpanQualifier, a span-centric scheme where spans, as opposed to tokens, are directly represented and scored to qualify as answers. Explicit span representations enable their interaction which exploits their dependencies to enhance representations. Experiments on three MSRC datasets demonstrate the effectiveness of our span-centric scheme and show that SpanQualifier achieves state-of-the-art results.
EFFECTS: Explorable and Explainable Feature Extraction Framework for Multivariate Time-Series Classification
Authors: Ido Ikar, Amit Somech
Venue: CIKM
Year: 2023

We demonstrate EFFECTS, an automated system for explorable and explainable feature extraction for multivariate time series classification. EFFECTS has a twofold contribution: (1) It significantly facilitates the exploration of MTSC data, and (2) it generates informative yet intuitive and explainable features to be used by the classification model. EFFECTS first mines the MTS data and extracts a set of interpretable features using an optimized transform-slice-aggregate process. To evaluate the quality of EFFECTS features, we gauge how well each feature distinguishes between every two classes, and how well they characterize each single class. Users can then explore the MTS data via the EFFECTS Explorer, which facilitates the visual inspection of important features, dimensions, and time slices. Last, the user can use the top features for each class when building a classification pipeline. We demonstrate EFFECTS on several real-world MTSC datasets, inviting the audience to investigate the data via EFFECTS Explorer and obtain initial insights on the time series data. Then, we will show how EFFECTS features are used in an ML model, and obtain accuracy that is on par with state-of-the-art MTSC models that do not optimize on explainability.
Proactive and Automatic Detection of Product Misclassifications at Massive Scale
Authors: Ling Jiang, Xiaoyu Chu, Saaransh Gulati, Pulkit Garg, Andrew Borthwick, Gang Luo
Venue: CIKM
Year: 2023

In e-commerce, product classification is widely used for various purposes. Misclassifying products can cause compliance issues and hurt the company's reputation. To address this problem, we propose an automated system to proactively detect product misclassifications by overcoming several challenges. A large e-commerce retailer can sell billions of distinct products, on which many thousands of classification tasks are performed. At this massive scale, we need to quickly detect misclassifications under a limited budget. In this talk, we point out these challenges and show how we design our system to handle them. When evaluated on a set of Amazon's product classification data, at an overhead of <10% of the classification cost, our system automatically identified and corrected many misclassifications, which would take a human many thousand years to manually find and 14.6 years to manually review and correct if our system were not used.
A Momentum Loss Reweighting Method for Improving Recall
Authors: Chenzhi Jiang, Yin Jin, Ningtao Wang, Ruofan Wu, Xing Fu, Weiqiang Wang
Venue: CIKM
Year: 2023

In many practical binary classification applications, such as financial fraud detection or medical diagnosis, it is crucial to optimize a model's performance on high-confidence samples whose scores are higher than a specific threshold, which is calculated by a given false positive rate according to practical requirements. However, the proportion of high-confidence samples is typically extremely small, especially in long-tailed datasets, which can lead to poor recall results and an alignment bias between realistic goals and loss. To address this challenge, we propose a novel loss reweighting framework called Momentum Threshold-Oriented Loss (MTOL) for binary classification tasks and propose two instantiated losses of it. Given a limited FPR range, MTOL aims to improve the recall of binary classification models at that FPR range by incorporating a batch memory queue and momentum estimation mechanisms. The MTOL adaptively estimates thresholds of FPR during the model training iterations and up-weights the loss of samples in the threshold range, with little consumption of storage and computation. Our experimental results on various datasets, including CIFAR-10, CIFAR-100, Tiny-ImageNet, demonstrate the significant effect of MTOL in improving the recall at low FPR especially in class imbalance settings. These results suggest that MTOL is a promising approach in scenarios where the model's performance in the low FPR range is of utmost importance.
Self-supervised Contrastive Enhancement with Symmetric Few-shot Learning Towers for Cold-start News Recommendation
Authors: Hao Jiang, Chuanzhen Li, Juanjuan Cai, Runyu Tian, Jingling Wang
Venue: CIKM
Year: 2023

Nowadays, news spreads faster than it is consumed. This, alongside the rapid news cycle and delayed updates, has led to a challenging news cold-start issue. Likewise, the user cold-start problem, due to limited user engagement, has long hindered recommendations. To tackle both of them, we introduce the Symmetric Few-shot Learning framework for Cold-start News Recommendation (SFCNR), built upon self-supervised contrastive enhancement. Our approach employs symmetric few-shot learning towers (SFTs) to transform warm user/news attributes into their behavior/content features during training. We design two innovative feature alignment strategies to enhance towers training. Subsequently, this tower generates virtual features for cold users/news during inference, leveraging tower-stored prior knowledge through a personalized gating network. We assess the SFCNR on four quality news recommendation models, conducting comprehensive experiments on two kinds of News dataset. Results showcase significant performance boosts for both warm and cold-start scenarios compared to baseline models.
Knowledge-Aware Cross-Semantic Alignment for Domain-Level Zero-Shot Recommendation
Authors: Junji Jiang, Hongke Zhao, Ming He, Likang Wu, Kai Zhang, Jianping Fan
Venue: CIKM
Year: 2023

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.
PriSHAP: Prior-guided Shapley Value Explanations for Correlated Features
Authors: Guanyu Jiang, Fuzhen Zhuang, Bowen Song, Tianyi Zhang, Deqing Wang
Venue: CIKM
Year: 2023

Among numerous explainable AI (XAI) methods proposed in recent years, model explanations based on Shapley values are widely accepted for their solid theoretical support from game theory. However, most existing methods approximate Shapley values based on a feature independence assumption considering the complexity of calculating exact Shapley values. This assumption could bring some counterfactual problems when interpreted features are highly correlated and result in explanations contrary to human intuition. In this paper, we propose PriSHAP to explicitly model the dependency relationship between correlated features and provide reasonable explanations for tabular data. Feature dependencies are analyzed and taken as prior information to guide the process of estimating Shapley values. Additionally, PriSHAP is free to be applied in popular Shapley value-based explainers to address counterfactual problems while providing more faithful explanations. A pipeline is given to apply PriSHAP in existing explainers with simple adjustments. Extensive experiments on both public datasets and artificial datasets are provided to demonstrate the effectiveness of our method.
Replace Scoring with Arrangement: A Contextual Set-to-Arrangement Framework for Learning-to-Rank
Authors: Jiarui Jin, Xianyu Chen, Weinan Zhang, Mengyue Yang, Yang Wang, Yali Du, Yong Yu, Jun Wang
Venue: CIKM
Year: 2023

Learning-to-rank is a core technique in the top-N recommendation task, where an ideal ranker would be a mapping from an item set to an arrangement (a.k.a. permutation). Most existing solutions fall in the paradigm of probabilistic ranking principle (PRP), i.e., first score each item in the candidate set and then perform a sort operation to generate the top ranking list. However, these approaches neglect the contextual dependence among candidate items during individual scoring, and the sort operation is non-differentiable. To bypass the above issues, we propose Set-To-Arrangement Ranking (STARank), a new framework directly generates the permutations of the candidate items without the need for individually scoring and sort operations; and is end-to-end differentiable. As a result, STARank can operate when only the ground-truth permutations are accessible without requiring access to the ground-truth relevance scores for items. For this purpose, STARank first reads the candidate items in the context of the user browsing history, whose representations are fed into a Plackett-Luce module to arrange the given items into a list. To effectively utilize the given ground-truth permutations for supervising STARank, we leverage the internal consistency property of Plackett-Luce models to derive a computationally efficient list-wise loss. Experimental comparisons against 9 the state-of-the-art methods on 2 learning-to-rank benchmark datasets and 3 top-N real-world recommendation datasets demonstrate the superiority of STARank in terms of conventional ranking metrics. Notice that these ranking metrics do not consider the effects of the contextual dependence among the items in the list, we design a new family of simulation-based ranking metrics, where existing metrics can be regarded as special cases. STARank can consistently achieve better performance in terms of PBM and UBM simulation-based metrics.
MSRA: A Multi-Aspect Semantic Relevance Approach for E-Commerce via Multimodal Pre-Training
Authors: Hanqi Jin, Jiwei Tan, Lixin Liu, Lisong Qiu, Shaowei Yao, Xi Chen, Xiaoyi Zeng
Venue: CIKM
Year: 2023

To enhance the effectiveness of matching user requests with millions of online products, practitioners invest significant efforts in developing semantic relevance models on large-scale e-commerce platforms. Generally, such semantic relevance models are formulated as text-matching approaches, which measure the relevance between users' search queries and the titles of candidate items (i.e., products). However, we argue that conventional relevance methods may lead to sub-optimal performance due to the limited information provided by the titles of candidate items. To alleviate this issue, we suggest incorporating additional information about candidate items from multiple aspects, including their attributes and images. This could supplement the information that may not be fully provided by titles alone. To this end, we propose a multi-aspect semantic relevance model that takes into account the match between search queries and the title, attribute and image information of items simultaneously. The model is further enhanced through pre-training using several well-designed self-supervised and weakly-supervised tasks. Furthermore, the proposed model is fine-tuned using annotated data and distilled into a representation-based architecture for efficient online deployment. Experimental results show the proposed approach significantly improves relevance and leads to considerable enhancements in business metrics.
Hierarchical Multi-Label Classification with Partial Labels and Unknown Hierarchy
Authors: Suhyeon Jo, DongHyeok Shin, Byeonghu Na, JoonHo Jang, Il-Chul Moon
Venue: CIKM
Year: 2023

Hierarchical multi-label classification aims at learning a multi-label classifier from a dataset whose labels are organized into a hierarchical structure. To the best of our knowledge, we propose for the first time the problem of finding a multi-label classifier given a partially labeled hierarchical multi-label dataset. We also assume the situation where the classifier cannot access hierarchical information during training. This work proposes an iterative framework for learning both multi-labels and a hierarchical structure of classes. When training a multi-label classifier from partial labels, our model extracts a class hierarchy from the classifier output using our hierarchy extraction algorithm. Then, our proposed loss exploits the extracted hierarchy to train the classifier. Theoretically, we show that our hierarchy extraction algorithm correctly finds the unknown hierarchy under a mild condition, and we prove that our loss function of multi-label classification with such hierarchy becomes an unbiased estimator of true multi-label classification risk. Our experiments show that our model obtains a class hierarchy close to the ground-truth dataset hierarchy, and simultaneously, our method outperforms previous methods for hierarchical multi-label classification and multi-label classification from partial labels.
SAFE: Sequential Attentive Face Embedding with Contrastive Learning for Deepfake Video Detection
Authors: Juho Jung, Chaewon Kang, Jeewoo Yoon, Simon S. Woo, Jinyoung Han
Venue: CIKM
Year: 2023

The emergence of hyper-realistic deepfake videos has raised significant concerns regarding their potential misuse. However, prior research on deepfake detection has primarily focused on image-based approaches, with little emphasis on video. With the advancement of generation techniques enabling intricate and dynamic manipulation of entire faces as well as specific facial components in a video sequence, capturing dynamic changes in both global and local facial features becomes crucial in detecting deepfake videos. This paper proposes a novel sequential attentive face embedding, SAFE, that can capture facial dynamics in a deepfake video. The proposed SAFE can effectively integrate global and local dynamics of facial features revealed in a video sequence using contrastive learning. Through a comprehensive comparison with the state-of-the-art methods on the DFDC (Deepfake Detection Challenge) dataset and the FaceForensic++ benchmark, we show that our model achieves the highest accuracy in detecting deepfake videos on both datasets.
CFOM: Lead Optimization For Drug Discovery With Limited Data
Authors: Natan Kaminsky, Uriel Singer, Kira Radinsky
Venue: CIKM
Year: 2023

Drug development is a long and costly process consisting of several stages that can take many years to complete. One of the early stage's goals is to optimize a novel chemical compound to be active against a target protein associated with the disease. Often machine learning techniques are used to improve the procedure of discovering and optimizing potential drug candidates. The goal of molecule optimization is, given an input molecule, to produce a new molecule that is chemically similar to the input molecule but with an improved property. We present a novel algorithm that during optimization divides a molecule into two disjoint substructures that we call: the molecule chains and the molecule core. Our approach is inspired by expert design of chemical compounds that employ a fundamental molecular template and add to it chemical functional groups to generate compounds with desired properties. We train a model to generate the molecule chains with the desired properties for optimization, which are then attached to the molecule core to construct a novel molecule with high similarity to the input molecule. This is achieved by selective masking of pairs of input molecules' chains and cores during training. Additionally, we demonstrate the extension of this approach to data-scarce tasks, like targeting a drug to a novel protein. We first evaluate our method on standard molecule optimization tasks such as inhibition against glycogen synthase kinase-3 beta (GSK3β). We then empirically compared the model performance with the state-of-the-art algorithms over 21 novel proteins and show superior performance.
Can a Chatbot be Useful in Childhood Cancer Survivorship? Development of a Chatbot for Survivors of Childhood Cancer
Authors: Mirae Kim, Kyubum Hwang, Hayoung Oh, Heejin Kim, Min-Ah Kim
Venue: CIKM
Year: 2023

This study introduces an informational and empathetic chatbot for childhood cancer survivors. As the survival rates for childhood cancer around the world have increased, survivors often face various psychosocial challenges during and after cancer treatment. However, they rarely seek support from psychosocial professionals due to the low availability of resources and stigma toward cancer survivors in countries like South Korea. This study aimed to develop a chatbot tailed to the unique characteristics of childhood cancer survivors in need of informational and emotional support. Given the limited availability of empirical data on childhood cancer survivors, quotes from survivors were gathered from academic articles and social media, then large language models were employed to generate appropriate responses. Furthermore, we incorporated domain learning techniques to ensure a more tailored and suitable model for addressing the needs of survivors.
Meta-Learning with Adaptive Weighted Loss for Imbalanced Cold-Start Recommendation
Authors: Minchang Kim, Yongjin Yang, Jung Hyun Ryu, Taesup Kim
Venue: CIKM
Year: 2023

Sequential recommenders have made great strides in capturing a user's preferences. Nevertheless, the cold-start recommendation remains a fundamental challenge as they typically involve limited user-item interactions for personalization. Recently, gradient-based meta-learning approaches have emerged in the sequential recommendation field due to their fast adaptation and easy-to-integrate abilities. The meta-learning algorithms formulate the cold-start recommendation as a few-shot learning problem, where each user is represented as a task to be adapted. While meta-learning algorithms generally assume that task-wise samples are evenly distributed over classes or values, user-item interactions in real-world applications do not conform to such a distribution (e.g., watching favorite videos multiple times, leaving only positive ratings without any negative ones). Consequently, imbalanced user feedback, which accounts for the majority of task training data, may dominate the user adaptation process and prevent meta-learning algorithms from learning meaningful meta-knowledge for personalized recommendations. To alleviate this limitation, we propose a novel sequential recommendation framework based on gradient-based meta-learning that captures the imbalanced rating distribution of each user and computes adaptive loss for user-specific learning. Our work is the first to tackle the impact of imbalanced ratings in cold-start sequential recommendation scenarios. Through extensive experiments conducted on real-world datasets, we demonstrate the effectiveness of our framework.
AmpliBias: Mitigating Dataset Bias through Bias Amplification in Few-shot Learning for Generative Models
Authors: Donggeun Ko, Dongjun Lee, Namjun Park, Kyoungrae Noh, Hyeonjin Park, Jaekwang Kim
Venue: CIKM
Year: 2023

Deep learning models exhibit a dependency on peripheral attributes of input data, such as shapes and colors, leading the models to become biased towards these certain attributes that result in subsequent degradation of performance. In this paper, we alleviate this problem by presenting~\sysname, a novel framework that tackles dataset bias by leveraging generative models to amplify bias and facilitate the learning of debiased representations of the classifier. Our method involves three major steps. We initially train a biased classifier, denoted as f_b, on a biased dataset and extract the top-K biased-conflict samples. Next, we train a generator solely on a bias-conflict dataset comprised of these top-K samples, aiming to learn the distribution of bias-conflict samples. Finally, we re-train the classifier on the newly constructed debiased dataset, which combines the original and amplified data. This allows the biased classifier to competently learn debiased representation. Extensive experiments validate that our proposed method effectively debiases the biased classifier.