Reviewerly/exHarmony huggingface dataset:
Knowledge-driven Analytics and Systems Impacting Human Quality of Life- Neurosymbolic AI, Explainable AI and Beyond

The management of knowledge-driven artificial intelligence technologies is essential in order to evaluate their impact on human life and society. Social networks and tech use can have a negative impact on us physically, emotionally, socially and mentally. On the other hand, intelligent systems can have a positive effect on people's lives. Currently, we are witnessing the power of large language models (LLMs) like chatGPT and its influence towards the society. The objective of the workshop is to contribute to the advancement of intelligent technologies designed to address the human condition. This could include precise and personalized medicine, better care for elderly people, reducing private data leaks, using AI to manage resources better, using AI to predict risks, augmenting human capabilities, and more. The workshop's objective is to present research findings and perspectives that demonstrate how knowledge-enabled technologies and applications improve human well-being. This workshop indeed focuses on the impacts at different granularity levels made by Artificial Intelligence (AI) research on the micro granular level, where the daily or regular functioning of human life is affected, and also the macro granulate level, where the long-term or far-future effects of artificial intelligence on people's lives and the human society could be pretty high. In conclusion, this workshop explores how AI research can potentially address the most pressing challenges facing modern societies, and how knowledge management can potentially contribute to these solutions.
Bias Invariant Approaches for Improving Word Embedding Fairness

Many public pre-trained word embeddings have been shown to encode different types of biases. Embeddings are often obtained from training on large pre-existing corpora, and therefore resulting biases can be a reflection of unfair representations in the original data. Bias, in this scenario, is a challenging problem since current mitigation techniques require knowing and understanding existing biases in the embedding, which is not always possible. In this work, we propose to improve word embedding fairness by borrowing methods from the field of data privacy. The idea behind this approach is to treat bias as if it were a special type of training data leakage. This has the unique advantage of not requiring prior knowledge of potential biases in word embeddings. We investigated two types of privacy algorithms, and measured their effect on bias using four different metrics. To investigate techniques from differential privacy, we applied Gaussian perturbation to public pre-trained word embeddings. To investigate noiseless privacy, we applied vector quantization during training. Experiments show that both approaches improve fairness for commonly used embeddings, and additionally, noiseless privacy techniques reduce the size of the resulting embedding representation.
G-STO: Sequential Main Shopping Intention Detection via Graph-Regularized Stochastic Transformer

Sequential recommendation requires understanding the dynamic patterns of users' behaviors, contexts, and preferences from their historical interactions. While most research emphasizes item-level user-item interactions, they often overlook underlying shopping intentions, such as preferences for ballpoint pens or miniatures. Identifying these latent intentions is vital for enhancing shopping experiences on platforms like Amazon. Despite its significance, the area of main shopping intention detection remains under-investigated in the academic literature. To fill this gap, we introduce a graph-regularized stochastic Transformer approach, G-STO. It considers intentions as product sets and user preferences as intention composites, both modeled as stochastic Gaussian embeddings in latent space. We also employ a global intention relational graph as prior knowledge for regularization, ensuring related intentions are distributionally close. These regularized embeddings are then input into Transformer-based models to capture sequential intention transitions. On testing our model with three real-world datasets, it outperformed the baselines by 18.08% in Hit@1, 7.01% in Hit@10, and 6.11% in NDCG@10.
POSPAN: Position-Constrained Span Masking for Language Model Pre-training

Span-level masked language modeling (MLM) has shown to be advantageous to pre-trained language models over the original single-token MLM, as entities/phrases and their dependencies are critical to language understanding. Previous works only consider span length with some discrete distributions, while the dependencies among spans are ignored, i.e., assuming that the positions of masked spans are uniformly distributed. In this paper, we present POSPAN, a general framework to allow diverse position-constrained span masking strategies via the combination of span length distribution and position constraint distribution, which unifies all existing span-level masking methods. To verify the effectiveness of POSPAN in pre-training, we evaluate it on the datasets from several NLU benchmarks. Experimental results indicate that the position constraint is capable of enhancing span-level masking broadly, and our best POSPAN setting consistently outperforms its span-length-only counterparts and vanilla MLM. We also conduct theoretical analysis for the position constraint in masked language models to shed light on the reason why POSPAN works well, demonstrating the rationality and necessity of POSPAN.
The First Workshop on Personalized Generative AI @ CIKM 2023: Personalization Meets Large Language Models

The First Workshop on Personalized Generative AI1 aims to be a cornerstone event fostering innovation and collaboration in the dynamic field of personalized AI. Leveraging the potent capabilities of Large Language Models (LLMs) to enhance user experiences with tailored responses and recommendations, the workshop is designed to address a range of pressing challenges including knowledge gap bridging, hallucination mitigation, and efficiency optimization in handling extensive user profiles. As a nexus for academics and industry professionals, the event promises rich discussions on a plethora of topics such as the development and fine-tuning of foundational models, strategies for multi-modal personalization, and the imperative ethical and privacy considerations in LLM deployment. Through a curated series of keynote speeches, insightful panel discussions, and hands-on sessions, the workshop aspires to be a catalyst in the development of more precise, contextually relevant, and user-centric AI systems. It aims to foster a landscape where generative AI systems are not only responsive but also anticipatory of individual user needs, marking a significant stride in personalized experiences.
Workshop on Document Intelligence Understanding

Document understanding and information extraction include different tasks to understand a document and extract valuable information automatically. Recently, there has been a rising demand for developing document understanding among different domains, including business, law, and medicine, to boost the efficiency of work that is associated with a large number of documents. This workshop aims to bring together researchers and industry developers in the field of document intelligence and understanding diverse document types to boost automatic document processing and understanding techniques. We also release a data challenge on the recently introduced document-level VQA dataset, PDFVQA. The PDFVQA challenge examines the model's structural and contextual understandings on the natural full document level of multiple consecutive document pages by including questions with a sequence of answers extracted from multi-pages of the full document. This task helps to boost the document understanding step from the single-page level to the full document level understanding.
GraphFC: Customs Fraud Detection with Label Scarcity

Customs officials across the world encounter huge volumes of transactions. Associated with customs transactions is customs fraud-the intentional manipulation of goods declarations to avoid taxes and duties. Due to limited manpower, the customs offices can only manually inspect a small number of declarations, necessitating the automation of customs fraud detection by machine learning techniques. The limited availability of manually inspected ground truth data makes it essential for the ML approach to generalize well on unseen data. However, current customs fraud detection models are not well suited or designed for this setting. In this work, we propose GraphFC (Graph Neural networks for Customs Fraud), a model-agnostic, domain-specific, graph neural network based customs fraud detection model that is designed to work in a real-world setting with limited ground truth data. Extensive experimentation using real customs data from two countries demonstrates that GraphFC generalizes well over unseen data and outperforms various baselines and other models by a large margin.
A Two-tier Shared Embedding Method for Review-based Recommender Systems

Reviews are valuable resources that have been widely researched and used to improve the quality of recommendation services. Recent methods use multiple full embedding layers to model various levels of individual preferences, increasing the risk of the data sparsity issue. Although it is a potential way to deal with this issue that models homophily among users who have similar behaviors, the existing approaches are implemented in a coarse-grained way. They calculate user similarities by considering the homophily in their global behaviors but ignore their local behaviors under a specific context. In this paper, we propose a two-tier shared embedding model (TSE), which fuses coarse- and fine-grained ways of modeling homophily. It considers global behaviors to model homophily in a coarse-grained way, and the high-level feature in the process of each user-item interaction to model homophily in a fine-grained way. TSE designs a whole-to-part principle-based process to fuse these ways in the review-based recommendation. Experiments on five real-world datasets demonstrate that TSE significantly outperforms state-of-the-art models. It outperforms the best baseline by 20.50% on the root-mean-square error (RMSE) and 23.96% on the mean absolute error (MAE), respectively. The source code is available at https://github.com/dianziliu/TSE.git.
Single-User Injection for Invisible Shilling Attack against Recommender Systems

Recommendation systems (RS) are crucial for alleviating the information overload problem. Due to its pivotal role in guiding users to make decisions, unscrupulous parties are lured to launch attacks against RS to affect the decisions of normal users and gain illegal profits. Among various types of attacks, shilling attack is one of the most subsistent and profitable attacks. In shilling attack, an adversarial party injects a number of well-designed fake user profiles into the system to mislead RS so that the attack goal can be achieved. Although existing shilling attack methods have achieved promising results, they all adopt the attack paradigm of multi-user injection, where some fake user profiles are required. This paper provides the first study of shilling attack in an extremely limited scenario: only one fake user profile is injected into the victim RS to launch shilling attacks (i.e., single-user injection). We propose a novel single-user injection method SUI-Attack for invisible shilling attack. SUI-Attack is a graph based attack method that models shilling attack as a node generation task over the user-item bipartite graph of the victim RS, and it constructs the fake user profile by generating user features and edges that link the fake user to items. Extensive experiments demonstrate that SUI-Attack can achieve promising attack results in single-user injection. In addition to its attack power, SUI-Attack increases the stealthiness of shilling attack and reduces the risk of being detected. We provide our implementation at: https://github.com/KDEGroup/SUI-Attack.
MUWS'2023: The 2nd International Workshop on Multimodal Human Understanding for the Web and Social Media

Multimodal human understanding and analysis is an emerging research area that cuts through several disciplines like Computer Vision, Natural Language Processing (NLP), Speech Processing, Human-Computer Interaction, and Multimedia. Several multimodal learning techniques have recently shown the benefit of combining multiple modalities in image-text, audio-visual and video representation learning and various downstream multimodal tasks. At the core, these methods focus on modelling the modalities and their complex interactions by using large amounts of data, different loss functions and deep neural network architectures. However, for many Web and Social media applications, there is the need to model the human, including the understanding of human behaviour and perception. For this, it becomes important to consider interdisciplinary approaches, including social sciences, semiotics and psychology. The core is understanding various cross-modal relations, quantifying bias such as social biases, and the applicability of models to real-world problems. Interdisciplinary theories such as semiotics or gestalt psychology can provide additional insights and analysis on perceptual understanding through signs and symbols via multiple modalities. In general, these theories provide a compelling view of multimodality and perception that can further expand computational research and multimedia applications on the Web and Social media.
Text Matching Improves Sequential Recommendation by Reducing Popularity Biases

This paper proposes Text mAtching based SequenTial rEcommenda-tion model (TASTE), which maps items and users in an embedding space and recommends items by matching their text representations. TASTE verbalizes items and user-item interactions using identifiers and attributes of items. To better characterize user behaviors, TASTE additionally proposes an attention sparsity method, which enables TASTE to model longer user-item interactions by reducing the self-attention computations during encoding. Our experiments show that TASTE outperforms the state-of-the-art methods on widely used sequential recommendation datasets. TASTE alleviates the cold start problem by representing long-tail items using full-text modeling and bringing the benefits of pretrained language models to recommendation systems. Our further analyses illustrate that TASTE significantly improves the recommendation accuracy by reducing the popularity bias of previous item id based recommendation models and returning more appropriate and text-relevant items to satisfy users. All codes are available at https://github.com/OpenMatch/TASTE.
DRL4IR: 4th Workshop on Deep Reinforcement Learning for Information Retrieval

\AcIR is one of the most important fields to help users find relevant information. The interaction between IR systems and users can be naturally formulated as a decision-making problem. In the last decade, deep reinforcement learning (DRL) has become a promising direction to utilize the high model capacity of deep learning to improve long-term gains. On the one hand, there have been emerging research works focusing on leveraging DRL for IR tasks while the fundamental information theory under DRL settings, the principle of RL methods for IR tasks, or the experimental evaluation protocols of DRL-based IR systems, has not been deeply investigated. On the other hand, the emerging ChatGPT also provides new insights and challenges for DRL-based IR.
HEPT Attack: Heuristic Perpendicular Trial for Hard-label Attacks under Limited Query Budgets

Exploring adversarial attacks on deep neural networks (DNNs) is crucial for assessing and enhancing their adversarial robustness. Among various attack types, hard-label attacks that rely only on predicted labels offer a practical approach. This paper focuses on the challenging task of hard-label attacks within an extremely limited query budget, which is a significant achievement rarely accomplished by existing methods. To tackle this, we propose an attack framework that leverages geometric information from previous perturbation directions to form triangles and employs a heuristic perpendicular trial to effectively utilize the intermediate directions. Extensive experiments validate the effectiveness of our approach under strict query constraints and demonstrate its superiority to the state-of-the-art methods.
Targeted Shilling Attacks on GNN-based Recommender Systems

GNN-based recommender systems have shown their vulnerability to shilling attacks in recent studies. By conducting shilling attacks on recommender systems, the attackers aim to have homogeneous impacts on all users. However, such indiscriminate attacks suffer from a waste of resources because even if the target item is promoted to users who are not interested, they are unlikely to click on them. In this paper, we conduct targeted shilling attacks in GNN-based recommender systems. By automatically constructing the features and edges of the fake users, our proposed framework AutoAttack achieves accurate attacks on a specific group of users while minimizing the impact on non-target users. Specifically, the features of fake users are generated based on a similarity function, which is optimized according to the features of target users. The structure of fake users is learned by conducting spectral clustering on the target users based on their graph Laplacian matrix, which contains the degree and adjacency information that provides guidance to the edge generation of fake users. We conduct extensive experiments on four real-world datasets in different GNN-based RS and evaluate the performance of our method on the shilling attack and recommendation tasks comprehensively, showing the effectiveness and flexibility of our framework.
Harnessing GPT for Topic-Based Call Segmentation in Microsoft Dynamics 365 Sales

Transcriptions of phone calls hold significant value in sales, customer service, healthcare, law enforcement, and more. However, analyzing recorded conversations can be a time-consuming process, especially for complex dialogues. In Microsoft Dynamics 365 Sales, a novel system, named GPT-Calls, is applied for efficient and accurate topic-based call segmentation. GPT-Calls comprises offline and online phases. In the offline phase, the system leverages a GPT model to generate synthetic sentences and extract anchor vectors for predefined topics. This phase, performed once on a given topic list, significantly reduces the computational burden. The online phase scores the similarity between the transcribed conversation and the topic anchors from the offline phase, followed by time domain analysis to group utterances into segments and tag them with topics. The GPT-Calls scheme offers an accurate and efficient approach to call segmentation and topic extraction, eliminating the need for labeled data. It is a versatile solution applicable to various industry domains. GPT-Calls operates in production under Dynamics 365 Sales Conversation Intelligence, applied to real sales conversations from diverse Dynamics 365 Sales tenants, streamlining call analysis, and saving time and resources while ensuring accuracy and effectiveness.
You're Not Alone in Battle: Combat Threat Analysis Using Attention Networks and a New Open Benchmark

For military commands, combat threat analysis is crucial in predicting future outcomes and informing consequent decisions. Its primary objectives include determining the intention and attack likelihood of the hostiles. The complex, dynamic, and noisy nature of combat, however, presents significant challenges in its analysis. The prior research has been limited in accounting for such characteristics, assuming independence of each entity, no unobserved tactics, and clean combat data. As such, we present spatio-temporal attention for threat analysis (SAFETY) to encode complex interactions that arise within combat. We test the model performance for unobserved tactics and with various perturbations. To do so, we also present the first open-source benchmark for combat threat analysis with two downstream tasks of predicting entity intention and attack probability. Our experiments show that SAFETY achieves a significant improvement in model performance, with enhancements of up to 13% in intention prediction and 7% in attack prediction compared to the strongest competitor, even when confronted with noisy or missing data. This result highlights the importance of encoding dynamic interactions among entities for combat threat analysis. Our codes and dataset are available at https://github.com/syleeheal/SAFETY.
CANA: Causal-enhanced Social Network Alignment

Social network alignment is widely applied in web applications for identifying corresponding nodes across different networks, such as linking users across two social networks. Existing methods for social network alignment primarily rely on alignment consistency, assuming that nodes with similar attributes and neighbors are more likely to be aligned. However, distributional discrepancies in node attributes and neighbors across different networks would bring biases in alignment consistency, leading to inferior alignment performance. To address this issue, we conduct a causal analysis of alignment consistency. Based on this analysis, we propose a novel model called CANA that uses causal inference approaches to mitigate biases and enhance social network alignment. Firstly, we disentangle observed node attributes into endogenous features and exogenous features with multi-task learning. Only endogenous features are retained to overcome node attribute discrepancies. To eliminate biases caused by neighbors discrepancies, we propose causal-aware attention mechanisms and integrate them in graph neural network to reweight contributions of different neighbors in alignment consistency comparison. Additionally, backdoor adjustment is applied to reduce confounding effects and estimate unbiased alignment probability. Through experimental evaluation on four real-world datasets, the proposed method demonstrates superior performance in terms of alignment accuracy and top-k hits precision.
The Role of Unattributed Behavior Logs in Predictive User Segmentation

Online browsing on firms' sites generates user behavior logs (or, logs). These logs are mainstays that drive several user modeling tasks. The logs that inform user modeling are the ones that are attributed to each user, termed Attributed Behaviors (AB). But, a lot more logs are anonymous, upwards of 85%. For example, many users do not sign in while browsing. These logs are not attributed to users, termed Unattributed Behaviors (UB), and are not recognized in user modeling. We examine whether and how UB can benefit user modeling. We focus on a common task, that of user segmentation, for which the prior art uses only AB. We demonstrate that information from UBs, although unattributed to any individual, when used along with ABs, enriches performance of machine learning model for user segmentation. We perform predictive segmentation, whereby predicted outcomes for each segment are evaluated against actual outcomes. Multiple evaluations on two datasets, one of which is public, relative to state of the art baseline, show strong performance of our model in predicting outcomes and in reducing user segmentation error.