ACM Transactions on Information Systems最新文献_第2页

FDKT: Towards an interpretable deep knowledge tracing via fuzzy reasoning FDKT：通过模糊推理实现可解释的深度知识追踪

IF 5.6 2区计算机科学

ACM Transactions on Information Systems Pub Date : 2024-04-05 DOI: 10.1145/3656167

Fei Liu, Chenyang Bu, Haotian Zhang, Le Wu, Kui Yu, Xuegang Hu

引用次数: 0

Personality-affected Emotion Generation in Dialog Systems 对话系统中受个性影响的情感生成

IF 5.6 2区计算机科学

ACM Transactions on Information Systems Pub Date : 2024-04-03 DOI: 10.1145/3655616

Zhiyuan Wen, Jiannong Cao, Jiaxing Shen, Ruosong Yang, Shuaiqi Liu, Maosong Sun

{"title":"Personality-affected Emotion Generation in Dialog Systems","authors":"Zhiyuan Wen, Jiannong Cao, Jiaxing Shen, Ruosong Yang, Shuaiqi Liu, Maosong Sun","doi":"10.1145/3655616","DOIUrl":"https://doi.org/10.1145/3655616","url":null,"abstract":"Generating appropriate emotions for responses is essential for dialog systems to provide human-like interaction in various application scenarios. Most previous dialog systems tried to achieve this goal by learning empathetic manners from anonymous conversational data. However, emotional responses generated by those methods may be inconsistent, which will decrease user engagement and service quality. Psychological findings suggest that the emotional expressions of humans are rooted in personality traits. Therefore, we propose a new task, Personality-affected Emotion Generation, to generate emotion based on the personality given to the dialog system and further investigate a solution through the personality-affected mood transition. Specifically, we first construct a daily dialog dataset, Personality EmotionLines Dataset (PELD), with emotion and personality annotations. Subsequently, we analyze the challenges in this task, i.e., (1) heterogeneously integrating personality and emotional factors and (2) extracting multi-granularity emotional information in the dialog context. Finally, we propose to model the personality as the transition weight by simulating the mood transition process in the dialog system and solve the challenges above. We conduct extensive experiments on PELD for evaluation. Results suggest that by adopting our method, the emotion generation performance is improved by 13% in macro-F1 and 5% in weighted-F1 from the BERT-base model.","PeriodicalId":50936,"journal":{"name":"ACM Transactions on Information Systems","volume":"50 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140583893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cross-domain NER under a Divide-and-Transfer Paradigm 分而治之范式下的跨域 NER

IF 5.6 2区计算机科学

ACM Transactions on Information Systems Pub Date : 2024-04-02 DOI: 10.1145/3655618

Xinghua Zhang, Bowen Yu, Xin Cong, Taoyu Su, Quangang Li, Tingwen Liu, Hongbo Xu

{"title":"Cross-domain NER under a Divide-and-Transfer Paradigm","authors":"Xinghua Zhang, Bowen Yu, Xin Cong, Taoyu Su, Quangang Li, Tingwen Liu, Hongbo Xu","doi":"10.1145/3655618","DOIUrl":"https://doi.org/10.1145/3655618","url":null,"abstract":"Cross-domain Named Entity Recognition (NER) transfers knowledge learned from a rich-resource source domain to improve the learning in a low-resource target domain. Most existing works are designed based on the sequence labeling framework, defining entity detection and type prediction as a monolithic process. However, they typically ignore the discrepant transferability of these two sub-tasks: the former locating spans corresponding to entities is largely domain-robust, while the latter owns distinct entity types across domains. Combining them into an entangled learning problem may contribute to the complexity of domain transfer. In this work, we propose the novel divide-and-transfer paradigm in which different sub-tasks are learned using separate functional modules for respective cross-domain transfer. To demonstrate the effectiveness of divide-and-transfer, we concretely implement two NER frameworks by applying this paradigm with different cross-domain transfer strategies. Experimental results on 10 different domain pairs show the notable superiority of our proposed frameworks. Experimental analyses indicate that significant advantages of the divide-and-transfer paradigm over prior monolithic ones originate from its better performance on low-resource data and a much greater transferability. It gives us a new insight into cross-domain NER. Our code is available at our github.","PeriodicalId":50936,"journal":{"name":"ACM Transactions on Information Systems","volume":"20 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140583912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Toward Bias-Agnostic Recommender Systems: A Universal Generative Framework 实现无偏见推荐系统：通用生成框架

IF 5.6 2区计算机科学

ACM Transactions on Information Systems Pub Date : 2024-04-02 DOI: 10.1145/3655617

Zhidan Wang, Lixin Zou, Chenliang Li, Shuaiqiang Wang, Xu Chen, Dawei Yin, Weidong Liu

{"title":"Toward Bias-Agnostic Recommender Systems: A Universal Generative Framework","authors":"Zhidan Wang, Lixin Zou, Chenliang Li, Shuaiqiang Wang, Xu Chen, Dawei Yin, Weidong Liu","doi":"10.1145/3655617","DOIUrl":"https://doi.org/10.1145/3655617","url":null,"abstract":"User behavior data, such as ratings and clicks, has been widely used to build personalizing models for recommender systems. However, many unflattering factors (e.g., popularity, ranking position, users’ selection) significantly affect the performance of the learned recommendation model. Most existing work on unbiased recommendation addressed these biases from sample granularity (e.g., sample reweighting, data augmentation) or from the perspective of representation learning (e.g., bias-modeling). However, these methods are usually designed for a specific bias, lacking the universal capability to handle complex situations where multiple biases co-exist. Besides, rare work frees itself from laborious and sophisticated debiasing configurations (e.g., propensity scores, imputed values, or user behavior-generating process). Towards this research gap, in this paper, we propose a universal Generative framework for Bias Disentanglement termed as GBD, constantly generating calibration perturbations for the intermediate representations during training to keep them from being affected by the bias. Specifically, a bias-identifier that tries to retrieve the bias-related information from the representations is first introduced. Subsequently, the calibration perturbations are generated to significantly deteriorate the bias-identifier’s performance, making the bias gradually disentangled from the calibrated representations. Therefore, without relying on notorious debiasing configurations, a bias-agnostic model is obtained under the guidance of the bias identifier. We further present its universality by subsuming the representative biases and their mixture under the proposed framework. Finally, extensive experiments on the real-world, synthetic, and semi-synthetic datasets have demonstrated the superiority of the proposed approach against a wide range of recommendation debiasing methods. The code is available at https://github.com/Zhidan-Wang/GBD.","PeriodicalId":50936,"journal":{"name":"ACM Transactions on Information Systems","volume":"46 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140584578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SSR: Solving Named Entity Recognition Problems via a Single-stream Reasoner SSR：通过单流推理器解决命名实体识别问题

IF 5.6 2区计算机科学

ACM Transactions on Information Systems Pub Date : 2024-04-01 DOI: 10.1145/3655619

Yuxiang Zhang, Junjie Wang, Xinyu Zhu, Tetsuya Sakai, Hayato Yamana

{"title":"SSR: Solving Named Entity Recognition Problems via a Single-stream Reasoner","authors":"Yuxiang Zhang, Junjie Wang, Xinyu Zhu, Tetsuya Sakai, Hayato Yamana","doi":"10.1145/3655619","DOIUrl":"https://doi.org/10.1145/3655619","url":null,"abstract":"Information Extraction (IE) focuses on transforming unstructured data into structured knowledge, of which Named Entity Recognition (NER) is a fundamental component. In the realm of Information Retrieval (IR), effectively recognizing entities can substantially enhance the precision of search and recommendation systems. Existing methods frame NER as a sequence labeling task, which requires extra data and, therefore may be limited in terms of sustainability. One promising solution is to employ a Machine Reading Comprehension (MRC) approach for NER tasks, thereby eliminating the dependence on additional data. This process encounters key challenges, including: 1) Unconventional predictions; 2) Inefficient multi-stream processing; 3) Absence of a proficient reasoning strategy. To this end, we present the Single-Stream Reasoner (SSR), a solution utilizing a reasoning strategy and standardized inputs. This yields a type-agnostic solution for both flat and nested NER tasks, without the need for additional data. On ten NER benchmarks, SSR achieved state-of-the-art results, highlighting its robustness. Furthermore, we illustrated its efficiency through convergence, inference speed, and low-resource scenario performance comparisons. Our architecture displays adaptability and can effortlessly merge with various foundational models and reasoning strategies, fostering advancements in both IR and IE fields.","PeriodicalId":50936,"journal":{"name":"ACM Transactions on Information Systems","volume":"51 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140585146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Beyond Relevance: Factor-level Causal Explanation for User Travel Decisions with Counterfactual Data Augmentation 超越相关性：利用反事实数据增强对用户旅行决策的因素级因果解释

IF 5.6 2区计算机科学

ACM Transactions on Information Systems Pub Date : 2024-03-22 DOI: 10.1145/3653673

Hanzhe Li, Jingjing Gu, Xinjiang Lu, Dazhong Shen, Yuting Liu, YaNan Deng, Guoliang Shi, Hui Xiong

{"title":"Beyond Relevance: Factor-level Causal Explanation for User Travel Decisions with Counterfactual Data Augmentation","authors":"Hanzhe Li, Jingjing Gu, Xinjiang Lu, Dazhong Shen, Yuting Liu, YaNan Deng, Guoliang Shi, Hui Xiong","doi":"10.1145/3653673","DOIUrl":"https://doi.org/10.1145/3653673","url":null,"abstract":"Point-of-Interest (POI) recommendation, an important research hotspot in the field of urban computing, plays a crucial role in urban construction. While understanding the process of users’ travel decisions and exploring the causality of POI choosing is not easy due to the complex and diverse influencing factors in urban travel scenarios. Moreover, the spurious explanations caused by severe data sparsity, i.e., misrepresenting universal relevance as causality, may also hinder us from understanding users’ travel decisions. To this end, in this paper, we propose a factor-level causal explanation generation framework based on counterfactual data augmentation for user travel decisions, named Factor-level Causal Explanation for User Travel Decisions (FCE-UTD), which can distinguish between true and false causal factors and generate true causal explanations. Specifically, we first assume that a user decision is composed of a set of several different factors. Then, by preserving the user decision structure with a joint counterfactual contrastive learning paradigm, we learn the representation of factors and detect the relevant factors. Next, we further identify true causal factors by constructing counterfactual decisions with a counterfactual representation generator, in particular, it can not only augment the dataset and mitigate the sparsity but also contribute to clarifying the causal factors from other false causal factors that may cause spurious explanations. Besides, a causal dependency learner is proposed to identify causal factors for each decision by learning causal dependency scores. Extensive experiments conducted on three real-world datasets demonstrate the superiority of our approach in terms of check-in rate, fidelity, and downstream tasks under different behavior scenarios. The extra case studies also demonstrate the ability of FCE-UTD to generate causal explanations in POI choosing.","PeriodicalId":50936,"journal":{"name":"ACM Transactions on Information Systems","volume":"21 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140201252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Listwise Generative Retrieval Models via a Sequential Learning Process 通过顺序学习过程建立列表式生成检索模型

IF 5.6 2区计算机科学

ACM Transactions on Information Systems Pub Date : 2024-03-22 DOI: 10.1145/3653712

Yubao Tang, Ruqing Zhang, Jiafeng Guo, Maarten de Rijke, Wei Chen, Xueqi Cheng

{"title":"Listwise Generative Retrieval Models via a Sequential Learning Process","authors":"Yubao Tang, Ruqing Zhang, Jiafeng Guo, Maarten de Rijke, Wei Chen, Xueqi Cheng","doi":"10.1145/3653712","DOIUrl":"https://doi.org/10.1145/3653712","url":null,"abstract":"Recently, a novel generative retrieval (GR) paradigm has been proposed, where a single sequence-to-sequence model is learned to directly generate a list of relevant document identifiers (docids) given a query. Existing generative retrieval (GR) models commonly employ maximum likelihood estimation (MLE) for optimization: this involves maximizing the likelihood of a single relevant docid given an input query, with the assumption that the likelihood for each docid is independent of the other docids in the list. We refer to these models as the pointwise approach in this paper. While the pointwise approach has been shown to be effective in the context of generative retrieval (GR), it is considered sub-optimal due to its disregard for the fundamental principle that ranking involves making predictions about lists. In this paper, we address this limitation by introducing an alternative listwise approach, which empowers the generative retrieval (GR) model to optimize the relevance at the docid list level. Specifically, we view the generation of a ranked docid list as a sequence learning process: at each step we learn a subset of parameters that maximizes the corresponding generation likelihood of the i-th docid given the (preceding) top i − 1 docids. To formalize the sequence learning process, we design a positional conditional probability for generative retrieval (GR). To alleviate the potential impact of beam search on the generation quality during inference, we perform relevance calibration on the generation likelihood of model-generated docids according to relevance grades. We conduct extensive experiments on representative binary and multi-graded relevance datasets. Our empirical results demonstrate that our method outperforms state-of-the-art generative retrieval (GR) baselines in terms of retrieval performance.","PeriodicalId":50936,"journal":{"name":"ACM Transactions on Information Systems","volume":"22 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140201406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Privacy-Preserving Cross-Domain Recommendation with Federated Graph Learning 利用联合图谱学习进行隐私保护跨域推荐

IF 5.6 2区计算机科学

ACM Transactions on Information Systems Pub Date : 2024-03-21 DOI: 10.1145/3653448

Changxin Tian, Yuexiang Xie, Xu Chen, Yaliang Li, Wayne Xin Zhao

{"title":"Privacy-Preserving Cross-Domain Recommendation with Federated Graph Learning","authors":"Changxin Tian, Yuexiang Xie, Xu Chen, Yaliang Li, Wayne Xin Zhao","doi":"10.1145/3653448","DOIUrl":"https://doi.org/10.1145/3653448","url":null,"abstract":"As people inevitably interact with items across multiple domains or various platforms, cross-domain recommendation (CDR) has gained increasing attention. However, the rising privacy concerns limit the practical applications of existing CDR models since they assume that full or partial data are accessible among different domains. Recent studies on privacy-aware CDR models neglect the heterogeneity from multiple domain data and fail to achieve consistent improvements in cross-domain recommendation; thus, it remains a challenging task to conduct effective CDR in a privacy-preserving way. In this paper, we propose a novel federated graph learning approach for Privacy-Preserving Cross-Domain Recommendation (denoted as PPCDR) to capture users’ preferences based on distributed multi-domain data and improve recommendation performance for all domains without privacy leakage. The main idea of PPCDR is to model both global preference among multiple domains and local preference at a specific domain for a given user, which characterizes the user’s shared and domain-specific tastes towards the items for interaction. Specifically, in the private update process of PPCDR, we design a graph transfer module for each domain to fuse global and local user preferences and update them based on local domain data. In the federated update process, through applying the local differential privacy (LDP) technique for privacy-preserving, we collaboratively learn global user preferences based on multi-domain data, and adapt these global preferences to heterogeneous domain data via personalized aggregation. In this way, PPCDR can effectively approximate the multi-domain training process that directly shares local interaction data in a privacy-preserving way. Extensive experiments on three CDR datasets demonstrate that PPCDR consistently outperforms competitive single- and cross-domain baselines and effectively protects domain privacy.","PeriodicalId":50936,"journal":{"name":"ACM Transactions on Information Systems","volume":"13 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140201241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Coupling Network For Multivariate Time Series Forecasting 用于多变量时间序列预测的深度耦合网络

IF 5.6 2区计算机科学

ACM Transactions on Information Systems Pub Date : 2024-03-21 DOI: 10.1145/3653447

Kun Yi, Qi Zhang, Hui He, Kaize Shi, Liang Hu, Ning An, Zhendong Niu

{"title":"Deep Coupling Network For Multivariate Time Series Forecasting","authors":"Kun Yi, Qi Zhang, Hui He, Kaize Shi, Liang Hu, Ning An, Zhendong Niu","doi":"10.1145/3653447","DOIUrl":"https://doi.org/10.1145/3653447","url":null,"abstract":"Multivariate time series (MTS) forecasting is crucial in many real-world applications. To achieve accurate MTS forecasting, it is essential to simultaneously consider both intra- and inter-series relationships among time series data. However, previous work has typically modeled intra- and inter-series relationships separately and has disregarded multi-order interactions present within and between time series data, which can seriously degrade forecasting accuracy. In this paper, we reexamine intra- and inter-series relationships from the perspective of mutual information and accordingly construct a comprehensive relationship learning mechanism tailored to simultaneously capture the intricate multi-order intra- and inter-series couplings. Based on the mechanism, we propose a novel deep coupling network for MTS forecasting, named DeepCN, which consists of a coupling mechanism dedicated to explicitly exploring the multi-order intra- and inter-series relationships among time series data concurrently, a coupled variable representation module aimed at encoding diverse variable patterns, and an inference module facilitating predictions through one forward step. Extensive experiments conducted on seven real-world datasets demonstrate that our proposed DeepCN achieves superior performance compared with the state-of-the-art baselines.","PeriodicalId":50936,"journal":{"name":"ACM Transactions on Information Systems","volume":"7 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140201661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Passage-aware Search Result Diversification 段落感知搜索结果多样化

IF 5.6 2区计算机科学

ACM Transactions on Information Systems Pub Date : 2024-03-21 DOI: 10.1145/3653672

Zhan Su, Zhicheng Dou, Yutao Zhu, Ji-Rong Wen

{"title":"Passage-aware Search Result Diversification","authors":"Zhan Su, Zhicheng Dou, Yutao Zhu, Ji-Rong Wen","doi":"10.1145/3653672","DOIUrl":"https://doi.org/10.1145/3653672","url":null,"abstract":"Research on search result diversification strives to enhance the variety of subtopics within the list of search results. Existing studies usually treat a document as a whole and represent it with one fixed-length vector. However, considering that a long document could cover different aspects of a query, using a single vector to represent the document is usually insufficient. To tackle this problem, we propose to exploit multiple passages to better represent documents in search result diversification. Different passages of each document may reflect different subtopics of the query and comparison among the passages can improve result diversity. Specifically, we segment the entire document into multiple passages and train a classifier to filter out the irrelevant ones. Then the document diversity is measured based on several passages that can offer the information needs of the query. Thereafter, we devise a passage-aware search result diversification framework that takes into account the topic information contained in the selected document sequence and candidate documents. The candidate documents’ novelty is evaluated based on their passages while considering the dynamically selected document sequence. We conducted experiments on a commonly utilized dataset, and the results indicate that our proposed method performs better than the most leading methods.","PeriodicalId":50936,"journal":{"name":"ACM Transactions on Information Systems","volume":"266 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140201405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0