ACM 信息系统交易 (TOIS) 发表有关信息检索（例如搜索引擎、推荐系统）的论文，其中包含：具有良好经验验证的新的原则性信息检索模型或算法；观察、实验和/或理论研究产生新的见解信息检索或信息搜索；现有信息检索技术的应用说明，阐明了这些技术的优缺点；新的信息检索或信息搜索任务的形式化以及评估这些任务性能的方法；内容的开发（文本、图像、语音、视频等）支持信息检索和信息搜索的分析方法；开发用户信息偏好和交互行为的计算模型；创建和分析信息检索和信息搜索的评估方法； ACM TRANSACTIONS ON INFORMATION SYSTEMS (TOIS) 的信息检索范围因其丰富的创意而吸引了行业从业者，并因其对同事工作的描述而吸引了学术研究人员。

ACM Transactions on Information Systems publishes research papers on information retrieval, such as search engines and recommendation systems. The content includes: well-validated new principled models or algorithms for information retrieval; new insights generated from observational, experimental, and theoretical studies; application examples of existing information retrieval technologies, highlighting their advantages and disadvantages; formalization of new information retrieval or search tasks and methods for evaluating their performance; analytical methods supporting content development (text, images, speech, video, etc.) for information retrieval and search; computational models of user information preferences and interaction behaviors; and the creation and analysis of evaluation methods for information retrieval and search. Due to its rich creativity and in-depth descriptions of peer work, ACM Transactions on Information Systems attracts the attention of both industry practitioners and academic researchers.

《ACM Transactions on Information Systems》发表有关信息检索（如搜索引擎、推荐系统）的研究论文。内容包括：经过良好经验验证的新信息检索模型或算法；通过观察、实验和理论研究获得的新见解；现有信息检索技术的应用示例，阐述其优缺点；新信息检索或信息搜索任务的形式化及其性能评估方法；支持信息检索和搜索的内容开发（文本、图像、语音、视频等）分析方法；用户信息偏好和交互行为的计算模型；信息检索和搜索评估方法的创建与分析。由于其丰富的创意和对同行研究的深入描述，《ACM Transactions on Information Systems》吸引了行业从业者和学术研究人员的关注。

ACM Transactions on Information Systems

Knowledge tracing models based on deep learning can achieve impressive predictive performance by leveraging attention mechanisms. However, there still exist two challenges in attentive knowledge tracing (AKT): First, the mechanism of classical models of AKT demonstrates relatively low attention when processing exercise sequences with shifting knowledge concepts (KC), making it difficult to capture the comprehensive state of knowledge across sequences. Second, classical models do not consider stochastic behaviors, which negatively affects models of AKT in terms of capturing anomalous knowledge states. This article proposes a model of AKT, called Enhancing Locality for Attentive Knowledge Tracing (ELAKT), that is a variant of the deep KT model. The proposed model leverages the encoder module of the transformer to aggregate knowledge embedding generated by both exercises and responses over all timesteps. In addition, it uses causal convolutions to aggregate and smooth the states of local knowledge. The ELAKT model uses the states of comprehensive KCs to introduce a prediction correction module to forecast the future responses of students to deal with noise caused by stochastic behaviors. The results of experiments demonstrated that the ELAKT model consistently outperforms state-of-the-art baseline KT models.

Yanjun Pu

Fang Liu

Rongye Shi

Haitao Yuan

Ruibo Chen

Tianhao Peng

Wenjun Wu

ACM Trans. Inf. Syst.

ELAKT: Enhancing Locality for Attentive Knowledge Tracing

ELAKT：增强局部性的注意力知识追踪

Recent years have witnessed the rapid accumulation of massive electronic medical records (EMRs), which highly support intelligent medical services such as drug recommendation. However, although there are multiple interaction types between drugs, e.g., synergism and antagonism, which can influence the effect of a drug package significantly, prior arts generally neglect the interaction between drugs or consider only a single type of interaction. Moreover, most existing studies generally formulate the problem of package recommendation as getting a personalized scoring function for users, despite the limits of discriminative models to achieve satisfactory performance in practical applications. To this end, in this paper, we propose a novel end-to-endDrugPackageGeneration (DPG) framework, which develops a new generative model for drug package recommendation with considering the interaction effect within drugs that affected by patient conditions. Specifically, we propose to formulate the drug package generation as a sequence generation process. Along this line, we first initialize the drug interaction graph based on medical records and domain knowledge. Then, we design a novel message passing neural network to capture the drug interaction, as well as a drug package generator based on a recurrent neural network. In detail, a mask layer is utilized to capture the impact of patient condition, and the deep reinforcement learning technique is leveraged to reduce the dependence on the drug order. Finally, extensive experiments on a real-world dataset from a first-rate hospital demonstrate the effectiveness of our DPG framework compared with several competitive baseline methods.

Zhi Zheng

Chao Wang

Tong Xu

Dazhong Shen

Penggang Qin

Xiangyu Zhao

Baoxing Huai

Xian Wu

Enhong Chen

Interaction-aware Drug Package Recommendation via Policy Gradient

基于策略梯度的交互感知药物包装推荐

Multi-behavior recommendation exploits multiple types of user-item interactions, such asviewandcart, to learn user preferences and has demonstrated to be an effective solution to alleviate the data sparsity problem faced by the traditional models that often utilize only one type of interaction for recommendation. In real scenarios, users often take a sequence of actions to interact with an item, in order to get more information about the item and thus accurately evaluate whether an item fits their personal preferences. Those interaction behaviors often obey a certain order, and more importantly, different behaviors reveal different information or aspects of user preferences towards the target item. Most existing multi-behavior recommendation methods take the strategy to first extract information from different behaviors separately and then fuse them for final prediction. However, they have not exploited the connections between different behaviors to learn user preferences. Besides, they often introduce complex model structures and more parameters to model multiple behaviors, largely increasing the space and time complexity. In this work, we propose a lightweight multi-behavior recommendation model namedCascading Residual Graph Convolutional Network(CRGCNfor short) for multi-behavior recommendation, which can explicitly exploit the connections between different behaviors into the embedding learning process without introducing any additional parameters (with comparison to the single-behavior based recommendation model). In particular, we design a cascading residualgraph convolutional network (GCN)structure, which enables our model to learn user preferences by continuously refining the embeddings across different types of behaviors. The multi-task learning method is adopted to jointly optimize our model based on different behaviors. Extensive experimental results on three real-world benchmark datasets show that CRGCN can substantially outperform the state-of-the-art methods, achieving 24.76%, 27.28%, and 25.10% relative gains on average in terms of HR@K (K = {10,20,50,80}) over the best baseline across the three datasets. Further studies also analyze the effects of leveraging multi-behaviors in different numbers and orders on the final performance.

Mingshi Yan

Zhiyong Cheng

Chen Gao

Jing Sun

Fan Liu

Fuming Sun

Haojie Li

Cascading Residual Graph Convolutional Network for Multi-Behavior Recommendation

多行为推荐的多级残差图卷积网络

Knowledge graphs (KGs) can help enhance recommendations, especially for the data-sparsity scenarios with limited user-item interaction data. Due to the strong power of representation learning of graph neural networks (GNNs), recent works of KG-based recommendation deploy GNN models to learn from both knowledge graph and user-item bipartite interaction graph. However, these works have not well considered thehierarchical structureof knowledge graph, leading to sub-optimal results. Despite the benefit of hierarchical structure, leveraging it is challenging since the structure is always partly-observed. In this work, we first propose to reveal unknown hierarchical structures with a supervised signal detection method and then exploit the hierarchical structure with disentangling representation learning. We conduct experiments on two large-scale datasets, of which the results well verify the superiority and rationality of the proposed method. Further experiments of ablation study with respect to key model designs have demonstrated the effectiveness and rationality of our proposed model. The code is available athttps://github.com/tsinghua-fib-lab/HIKE.

Yingrong Qin

Shuangqing Wei

Yue Wang

Depeng Jin

Jian Yuan

Lin Zhang

Dong Li

Jianye Hao

Yong Li

Learning from Hierarchical Structure of Knowledge Graph for Recommendation

从知识图谱的层次结构中学习以进行推荐

Social Event Detection (SED) aims to identify significant events from social streams, and has a wide application ranging from public opinion analysis to risk management. In recent years, Graph Neural Network (GNN) based solutions have achieved state-of-the-art performance. However, GNN-based methods often struggle with missing and noisy edges between messages, affecting the quality of learned message embedding. Moreover, these methods statically initialize node embedding before training, which, in turn, limits the ability to learn from message texts and relations simultaneously. In this paper, we approach social event detection from a new perspective based on Pre-trained Language Models (PLMs), and present\(\mathrm{RPLM}_{SED}\)(Relational prompt-basedPre-trainedLanguageModels forSocialEventDetection). We first propose a new pairwise message modeling strategy to construct social messages into message pairs with multi-relational sequences. Secondly, a new multi-relational prompt-based pairwise message learning mechanism is proposed to learn more comprehensive message representation from message pairs with multi-relational prompts using PLMs. Thirdly, we design a new clustering constraint to optimize the encoding process by enhancing intra-cluster compactness and inter-cluster dispersion, making the message representation more distinguishable. We evaluate the\(\mathrm{RPLM}_{SED}\)on three real-world datasets, demonstrating that the\(\mathrm{RPLM}_{SED}\)model achieves state-of-the-art performance in offline, online, low-resource, and long-tail distribution scenarios for social event detection tasks.

Pu Li

Xiaoyan Yu

Hao Peng

Yantuan Xian

Linqin Wang

Li Sun

Jingyun Zhang

Philip S. Yu

Relational Prompt-based Pre-trained Language Models for Social Event Detection

基于关系提示的预训练语言模型用于社交事件检测

Unleashing the power of image-text matching in real-world applications is hampered by noisy correspondence. Manually curating high-quality datasets is expensive and time-consuming, and datasets generated using diffusion models are not adequately well-aligned. The most promising way is to collect image-text pairs from the Internet, but it will inevitably introduce noisy correspondence. To reduce the negative impact of noisy correspondence, we propose a novel model that first transforms the noisy correspondence filtering problem into a similarity distribution modeling problem by exploiting the powerful capabilities of pre-trained models. Specifically, we use the Gaussian Mixture model to model the similarity obtained by CLIP as clean distribution and noisy distribution, to filter out most of the noisy correspondence in the dataset. Afterward, we used relatively clean data to fine-tune the model. To further reduce the negative impact of unfiltered noisy correspondence, i.e., a minimal part where two distributions intersect during the fine-tuning process, we propose a distribution-sensitive dynamic margin ranking loss, further increasing the distance between the two distributions. Through continuous iteration, the noisy correspondence gradually decreases and the model performance gradually improves. Our extensive experiments demonstrate the effectiveness and robustness of our model even under high noise rates.

Haitao Shi

Meng Liu

Xiaoxuan Mu

Xuemeng Song

Yupeng Hu

Liqiang Nie

Breaking Through the Noisy Correspondence: A Robust Model for Image-Text Matching

突破噪声对应关系：一种鲁棒的图像-文本匹配模型

With the rapid development of online services and web applications, recommender systems (RS) have become increasingly indispensable for mitigating information overload and matching users’ information needs by providing personalized suggestions over items. Although the RS research community has made remarkable progress over the past decades, conventional recommendation models (CRM) still have some limitations,e.g., lacking open-domain world knowledge, and difficulties in comprehending users’ underlying preferences and motivations. Meanwhile, large language models (LLM) have shown impressive general intelligence and human-like capabilities for various natural language processing (NLP) tasks, which mainly stem from their extensive open-world knowledge, logical and commonsense reasoning abilities, as well as their comprehension of human culture and society. Consequently, the emergence of LLM is inspiring the design of recommender systems and pointing out a promising research direction,i.e., whether we can incorporate LLM and benefit from their common knowledge and capabilities to compensate for the limitations of CRM. In this paper, we conduct a comprehensive survey on this research direction, and draw a bird’s-eye view from the perspective of the whole pipeline in real-world recommender systems. Specifically, we summarize existing research works from two orthogonal aspects: where and how to adapt LLM to RS. For the “WHERE” question, we discuss the roles that LLM could play in different stages of the recommendation pipeline,i.e., feature engineering, feature encoder, scoring/ranking function, user interaction, and pipeline controller. For the “HOW” question, we investigate the training and inference strategies, resulting in two fine-grained taxonomy criteria,i.e., whether to tune LLM or not during training, and whether to involve conventional recommendation models for inference. Detailed analysis and general development paths are provided for both “WHERE” and “HOW” questions, respectively. Then, we highlight the key challenges in adapting LLM to RS from three aspects,i.e., efficiency, effectiveness, and ethics. Finally, we summarize the survey and discuss the future prospects. To further facilitate the research community of LLM-enhanced recommender systems, we actively maintain a GitHub repository for papers and other related resources in this rising direction1.

Jianghao Lin

Xinyi Dai

Yunjia Xi

Weiwen Liu

Bo Chen

Hao Zhang

Yong Liu

Chuhan Wu

Xiangyang Li

Chenxu Zhu

Huifeng Guo

Yong Yu

Ruiming Tang

Weinan Zhang

How Can Recommender Systems Benefit from Large Language Models: A Survey

推荐系统如何从大型语言模型中获益：一项调查

Contrastive learning has recently emerged as an effective strategy for improving the performance of sequential recommendation. However, traditional models commonly construct the contrastive loss by directly optimizing human-designed positive and negative samples, resulting in a model that is overly sensitive to heuristic rules. To address this limitation, we propose a novel soft contrastive framework for sequential recommendation in this paper. Our main idea is to extend the point-wise contrast to a region-level comparison, where we aim to identify instances near the initially selected positive/negative samples that exhibit similar contrastive properties. This extension improves the model’s robustness to human heuristics. To achieve this objective, we introduce an adversarial contrastive loss that allows us to explore the sample regions more effectively. Specifically, we begin by considering the user behavior sequence as a holistic entity. We construct adversarial samples by introducing a continuous perturbation vector to the sequence representation. This perturbation vector adds variability to the sequence, enabling more flexible exploration of the sample regions. Moreover, we extend the aforementioned strategy by applying perturbations directly to the items within the sequence. This accounts for the sequential nature of the items. To capture these sequential relationships, we utilize a recurrent neural network to associate the perturbations, which introduces an inductive bias for more efficient exploration of adversarial samples. To demonstrate the effectiveness of our model, we conduct extensive experiments on five real-world datasets.

Yabin Zhang

Zhenlei Wang

Wenhui Yu

Lantao Hu

Peng Jiang

Kun Gai

Xu Chen

Soft Contrastive Sequential Recommendation

软对比序列推荐

Generating appropriate emotions for responses is essential for dialogue systems to provide human-like interaction in various application scenarios. Most previous dialogue 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 dialogue system and further investigate a solution through the personality-affected mood transition. Specifically, we first construct a daily dialogue 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 dialogue context. Finally, we propose to model the personality as the transition weight by simulating the mood transition process in the dialogue 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 by13%in macro-F1 and5%in weighted-F1 from the BERT-base model.

Abbreviation	ACM Trans. Inf. Syst.
Journal Impact	5.36
Quartiles(Global)	COMPUTER SCIENCE, INFORMATION SYSTEMS(Q1)

ISSN	1046-8188, 1558-2868
h-index	95

Publication Information	Publisher: Association for Computing Machinery (ACM)，Publishing cycle: Quarterly，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 1983，Proportion of original research papers: 96.51%，Self Citation Rate:14.80%， Gold OA Rate: 1.23%
Average review cycle	网友分享经验：>12周，或约稿
Average recruitment ratio	网友分享经验：容易

ACM Transactions on Information Systems

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ELAKT: Enhancing Locality for Attentive Knowledge Tracing

Interaction-aware Drug Package Recommendation via Policy Gradient

Cascading Residual Graph Convolutional Network for Multi-Behavior Recommendation

Learning from Hierarchical Structure of Knowledge Graph for Recommendation

Relational Prompt-based Pre-trained Language Models for Social Event Detection

Relational Prompt-based Pre-trained Language Models for Social Event Detection

Breaking Through the Noisy Correspondence: A Robust Model for Image-Text Matching

How Can Recommender Systems Benefit from Large Language Models: A Survey

Soft Contrastive Sequential Recommendation

Personality-affected Emotion Generation in Dialog Systems