OpenRaiser/Intern-Atlas huggingface dataset:
Collaborative Alignment for Recommendation
Authors: Chen Wang, Liangwei Yang, Zhiwei Liu, Xiaolong Liu, Mingdai Yang, Yueqing Liang, Philip S. Yu
Venue: CIKM
Year: 2024

Traditional recommender systems have primarily relied on identity representations (IDs) to model users and items. Recently, the integration of pre-trained language models (PLMs) has enhanced the capability to capture semantic descriptions of items. However, while PLMs excel in few-shot, zero-shot, and unified modeling scenarios, they often overlook the crucial signals from collaborative filtering (CF), resulting in suboptimal performance when sufficient training data is available. To effectively combine semantic representations with the CF signal and enhance recommender system performance in both warm and cold settings, two major challenges must be addressed: (1) bridging the gap between semantic and collaborative representation spaces, and (2) refining while preserving the integrity of semantic representations. In this paper, we introduce CARec, a novel model that adeptly integrates collaborative filtering signals with semantic representations, ensuring alignment within the semantic space while maintaining essential semantics. We present experimental results from four real-world datasets, which demonstrate significant improvements. By leveraging collaborative alignment, CARec also shows remarkable effectiveness in cold-start scenarios, achieving notable enhancements in recommendation performance. The code is available at https://github.com/ChenMetanoia/CARec **REMOVE 2nd URL**://github.com/ChenMetanoia/CARec.
Sparks of Surprise: Multi-objective Recommendations with Hierarchical Decision Transformers for Diversity, Novelty, and Serendipity
Authors: Jie Wang, Alexandros Karatzoglou, Ioannis Arapakis, Xin Xin, Xuri Ge, Joemon M. Jose
Venue: CIKM
Year: 2024

Personalized Session-based Recommendation (PSR) extends the traditional sequential recommendation models-which typically recommends the next item based on a recent active session-to leverage historical sessions of a user for short-term recommendations in current session. However, existing PSR methods face two limitations: (1) treating offline sessions uniformly as static data and relying on user embeddings to represent personalized information overlook the dynamic evolution of interests over time, which can change significantly as sessions progress in practical application. (2) focusing on accuracy, i.e., recommending items relevant to recent interactions, ignores the balance of multi-faceted requirements for user satisfaction, i.e., diversity, novelty, and serendipity. Therefore, we introduce Multi-objective PSR (MOPSR) task and propose Hierarchical Decision Transformers (HDT) framework, which models strictly sequential preference transitions of users across and within sessions to balance recommendation accuracy with the mentioned objectives. To address the first problem, Inter-session DT dynamically tracks the user's long-term preference across sessions by maintaining a goal state. This goal state serves as personalized information to collaboratively make recommendations with short-term state via the Intra-session DT. To tackle the second limitation, we propose inter-session and intra-session unexpected returns to trade off relevant recommendations and user preferences on diversity, novelty, and serendipity. The hierarchical returns help the recommender accurately identify signals of the user's expectations and changes in multi-objective preferences. To verify the effectiveness of our method on the MOPSR, we apply HDT to four state-of-the-art sequential recommendation models and conduct experiments on two publicly available datasets. Experimental results demonstrate that (1) HDT can widely generalize sequential models to solve the MOPSR task in scenarios with incrementally generated sessions, and (2) our method can balance multi-objectives by maintaining and even enhancing accuracy while effectively improving the diversity, novelty, and serendipity objectives.
DTFormer: A Transformer-Based Method for Discrete-Time Dynamic Graph Representation Learning
Authors: Xi Chen, Yun Xiong, Siwei Zhang, Jiawei Zhang, Yao Zhang, Shiyang Zhou, Xixi Wu, Mingyang Zhang, Tengfei Liu, Weiqiang Wang
Venue: CIKM
Year: 2024

Discrete-Time Dynamic Graphs (DTDGs), which are prevalent in real-world implementations and notable for their ease of data acquisition, have garnered considerable attention from both academic researchers and industry practitioners. The representation learning of DTDGs has been extensively applied to model the dynamics of temporally changing entities and their evolving connections. Currently, DTDG representation learning predominantly relies on GNN+RNN architectures, which manifest the inherent limitations of both Graph Neural Networks (GNNs) and Recurrent Neural Networks (RNNs). GNNs suffer from the over-smoothing issue as the models architecture goes deeper, while RNNs struggle to capture long-term dependencies effectively. GNN+RNN architectures also grapple with scaling to large graph sizes and long sequences. Additionally, these methods often compute node representations separately and focus solely on individual node characteristics, thereby overlooking the behavior intersections between the two nodes whose link is being predicted, such as instances where the two nodes appear together in the same context or share common neighbors. This paper introduces a novel representation learning method øurs~for DTDGs, pivoting from the traditional GNN+RNN framework to a Transformer-based architecture. Our approach exploits the attention mechanism to concurrently process topological information within the graph at each timestamp and temporal dynamics of graphs along the timestamps, circumventing the aforementioned fundamental weakness of both GNNs and RNNs. Moreover, we enhance the model's expressive capability by incorporating the intersection relationships among nodes and integrating a multi-patching module. Extensive experiments conducted on six public dynamic graph benchmark datasets confirm our model's efficacy, achieving the SOTA performance.
Enhancing Event Detection with Inter-Event Dependencies in Large Ontologies
Authors: Samireh Abdi
Venue: CIKM
Year: 2024

Event Detection (ED), a crucial component of comprehensive text analysis tools, is a well-established task within the fields of Natural Language Processing (NLP) and Information Extraction (IE). Current state-of-the-art models for ED primarily focus on identifying a limited set of predefined event types. Recently, the challenge of detecting a broad array of predefined event types has garnered increasing interest within the IE community. However, a significant gap in existing research on ED with extensive ontologies is the inadequate exploration of how interactions between event types affect ED model performance. One of the hindrances for this purpose is the lack of resources to encode event-event dependencies for large ontologies. This study introduces a novel approach that leverages existing inter-event dependency resources to provide this information for extensive ontologies. Specifically, a solution based on Optimal Transport is proposed to map event-event dependency from existing resources to a large ontology. We conduct extensive experiments on multiple benchmark datasets to assess the effectiveness of our approach. Our findings, supported by a thorough analysis, demonstrate that this innovative technique significantly enhances the performance of ED models, especially for ontologies with a large number of event types.
A Geometric Perspective for High-Dimensional Multiplex Graphs
Authors: Kamel Abdous, Nairouz Mrabah, Mohamed Bouguessa
Venue: CIKM
Year: 2024

High-dimensional multiplex graphs are characterized by their high number of complementary and divergent dimensions. The existence of multiple hierarchical latent relations between the graph dimensions poses significant challenges to embedding methods. In particular, the geometric distortions that might occur in the representational space have been overlooked in the literature. This work studies the problem of high-dimensional multiplex graph embedding from a geometric perspective. We find that the node representations reside on highly curved manifolds, thus rendering their exploitation more challenging for downstream tasks. Moreover, our study reveals that increasing the number of graph dimensions can cause further distortions to the highly curved manifolds. To address this problem, we propose a novel multiplex graph embedding method that harnesses hierarchical dimension embedding and Hyperbolic Graph Neural Networks. The proposed approach hierarchically extracts hyperbolic node representations that reside on Riemannian manifolds while gradually learning fewer and more expressive latent dimensions of the multiplex graph. Experimental results on real-world high-dimensional multiplex graphs show that the synergy between hierarchical and hyperbolic embeddings incurs much fewer geometric distortions and brings notable improvements over state-of-the-art approaches on downstream tasks.
GeoAI for Natural Disaster Assessment
Authors: Saugat Adhikari
Venue: CIKM
Year: 2024

Climate change has led to a sharp increase in the number and severity of extreme events, such as floods, tornados and wildfires. These events have resulted in adverse effects on human lives and the infrastructure. Swift disaster assessment is crucial for the effective planning of disaster response and relief efforts. AI and big data have provided unprecedented opportunities to enable swift disaster assessment, but two significant hurdles exist: (1) the scarcity of annotated geospatial data to train AI models, and (2) the lack of AI solutions that encode physics knowledge in a geospatial context. My research aims to address both challenges by developing an active-learning-based annotation platform that improves the annotation productivity of geospatial data for geospatial machine learning, and by developing physics-guided machine learning models for accurate natural disaster assessment.
EDGE: A Conversational Interface driven by Large Language Models for Educational Knowledge Graphs Exploration
Authors: Neda Afreen, Giacomo Balloccu, Ludovico Boratto, Gianni Fenu, Francesca Maridina Malloci, Mirko Marras, Andrea Giovanni Martis
Venue: CIKM
Year: 2024

As education adopts digital platforms, the vast amount of information from various sources, such as learning management systems and learning object repositories, presents challenges in navigation and elaboration. Traditional interfaces involve a steep learning curve, limited user accessibility, and lack flexibility. Language models alone cannot address these issues as they do not have access to structured information specific to the educational organization. In this paper, we propose EDGE (EDucational knowledge Graph Explorer), a natural language interface that uses knowledge graphs to organize educational information. EDGE translates natural language requests into queries and converts the results back into natural language responses. We show EDGE's versatility using knowledge graphs built from public datasets, providing example interactions of different stakeholders. Demo video: https://u.garr.it/eYq63.
Ensembles for Outlier Detection and Evaluation
Authors: Charu C. Aggarwal
Venue: CIKM
Year: 2024

Ensemble methods are widely used in many machine learning applications such as classification and recommender systems. However, ensemble methods have been slow to develop in unsupervised domains such as outlier detection[1]. The earliest methods for outlier ensemble analysis include techniques such as feature bagging and isolation forests[5,6]. Subsequently, theoretical foundations were developed for outlier ensembles[2], which turned out to be analogous to those used in classification. Therefore, many outlier ensemble methods from classification can be generalized to outlier detection. However, the unsupervised nature of the outlier detection problem necessitates some changes to these algorithms. For example, subsampling methods need to be replaced by variable subsampling in order to obtain the best results[2]. This is because variable subsampling methods implicitly explore the parameter space over different base detectors so that problems associated with lack of supervision are addressed. Outlier ensemble methods can be either data-centric (in which components use different subsets or subspaces of the data) or they could be model-centric (in which components use different variations of model-centric design). Examples of data-centric methods include methods like feature bagging and subsampling, whereas examples of model-centric methods include Isolation Forests[6], RandNet [4], and Subspace Histograms[7]. However, techniques like variable subsampling seem to have characteristics of both types of ensembles. Unsupervised algorithms like outlier are often hard to evaluate because different algorithms may perform better for different choices of parameters. In general, it is not fair to compare base detectors with ensembles, since techniques like variable subsampling almost always improve performance. In such cases, outlier ensembles could be used for evaluation of outlier detection algorithms[3] by wrapping the base detectors in variable subsampling. A large number of base detectors and their ensemble-centric versions were compared, and the correlations between different detectors was analyzed. This analysis was used to propose TRINITY[3], which is an ensemble-of-ensembles detector --- this detector combines the variable subsampling versions of three base detectors and seems to be very robust over a wide variety of data sets. Recently, outlier ensembles have also been used for meta-learning[8]. It is shown how a transfer resource of labeled data sets can be used to combine scores optimally from different detectors for a new unlabeled data set.
Accurate Path Prediction of Provenance Traces
Authors: Raza Ahmad, Hee-Young Jung, Yuta Nakamura, Tanu Malik
Venue: CIKM
Year: 2024

Several security and workflow applications require provenance information at the operating system level for diagnostics. The resulting provenance traces are often more informative if they are efficiently mapped to execution paths within the control flow graph. However, current provenance systems do not map traces to control flow graphs for diagnostics purposes due to the computational complexity of mapping traces to graphs. We formulate the path prediction problem for provenance traces and take a machine learning approach to solve the problem. We develop a transformer-based graph convolutional network to predict paths. Our experiments demonstrate that our machine learning model achieves more than twice the accuracy on average compared to simple probabilistic models, with an increased computation time trade-off.
XplainScreen: Unveiling the Black Box of Graph Neural Network Drug Screening Models with a Unified XAI Framework
Authors: Geonhee Ahn, Md. Mahim Anjum Haque, Subhashis Hazarika, Soo Kyung Kim
Venue: CIKM
Year: 2024

Despite the powerful capabilities of GNN-based drug screening model in predicting target drug properties, the black-box nature of these models poses a challenge for practical application, particularly in a field as critical as drug development where understanding and trust in AI-driven decisions are important. To address the interpretability issues associated with GNN-based virtual drug screening, we introduce XplainScreen: a unified explanation framework designed to evaluate various explanation methods for GNN-based models. XplainScreen offers a user-friendly, web-based interactive platform that allows for the selection of specific GNN-based drug screening models and multiple cutting-edge explainable AI methods. It supports both qualitative assessments (through visualization and generative text descriptions) and quantitative evaluations of these methods, utilizing drug molecules in SMILES format. This demonstration showcases the utility of XplainScreen through a user study with pharmacological researchers focused on virtual screening tasks based on toxicity, highlighting the framework's potential to enhance the integrity and trustworthiness of AI-driven virtual drug screening. A video demo of XplainScreen is available at https://youtu.be/Q4yobrTLKec, and the source code can be accessed at https://github.com/GeonHeeAhn/XplainScreen.
Neural Additive Tensor Decomposition for Sparse Tensors
Authors: Dawon Ahn, Uday Singh Saini, Evangelos E. Papalexakis, Ali Payani
Venue: CIKM
Year: 2024

Given a sparse tensor, how can we accurately capture complex latent structures inherent in the tensor while maintaining the interpretability of those structures? Tensor decomposition is a fundamental technique for analyzing tensors. Classical tensor models provide multi-linear structures that are easy to interpret, but have limitations in capturing complex structures present in real-world sparse tensors. Recent neural tensor models have extended the capabilities of classical tensor models in capturing complex structures within the data. However, this has come at the cost of interpretability: neural tensor models entangle interactions across and within latent structures in a black-box manner, making it difficult to readily understand the discovered structures. Understanding these structures, however, is crucial in applications such as healthcare, which requires transparency in critical decision-making processes. To overcome this major limitation and bridge the gap between the classical multi-linear models and neural tensor models, we propose Neural Additive Tensor Decomposition (NeAT), an accurate and interpretable neural tensor model for sparse tensors. The main idea of NeAT is to apply neural networks to each latent component in an additive fashion. This not only captures diverse patterns and complex structures in sparse tensors, but also provides a direct and intuitive interpretation of the structures by being close to the multi-linear tensor model. We conduct extensive experiments on six large real-world sparse tensors. NeAT outperforms the state-of-the-art neural tensor models in link prediction, surpassing a linear tensor model by 10% and the second-best neural tensor model by 4%, in accuracy. Through ablation studies, we explore various model designs for NeAT to identify key factors that impact generalization. Finally, we evaluate qualitatively and quantitatively latent patterns discovered by NeAT, demonstrating how to analyze the discovered latent patterns in real data obtained from NeAT.
Navigating the Landscape of Reproducible Research: A Predictive Modeling Approach
Authors: Akhil Pandey Akella, Sagnik Ray Choudhury, David Koop, Hamed Alhoori
Venue: CIKM
Year: 2024

The reproducibility of scientific articles is central to the advancement of science. Despite this importance, evaluating reproducibility remains challenging due to the scarcity of ground truth data. Predictive models can address this limitation by streamlining the tedious evaluation process. Typically, a paper's reproducibility is inferred based on the availability of artifacts such as code, data, or supplemental information, often without extensive empirical investigation. To address these issues, we utilized artifacts of papers as fundamental units to develop a novel, dual-spectrum framework that focuses on author-centric and external-agent perspectives. We used the author-centric spectrum, followed by the external-agent spectrum, to guide a structured, model-based approach to quantify and assess reproducibility. We explored the interdependencies between different factors influencing reproducibility and found that linguistic features such as readability and lexical diversity are strongly correlated with papers achieving the highest statuses on both spectrums. Our work provides a model-driven pathway for evaluating the reproducibility of scientific research.
UGAD: Universal Generative AI Detector utilizing Frequency Fingerprints
Authors: Inzamamul Alam, Muhammad Shahid Muneer, Simon S. Woo
Venue: CIKM
Year: 2024

In the wake of a fabricated explosion image at the Pentagon, an ability to discern real images from fake counterparts has never been more critical. Our study introduces a novel multi-modal approach to detect AI-generated images amidst the proliferation of new generation methods such as Diffusion models. Our method, UGAD, encompasses three key detection steps: First, we transform the RGB images into YCbCr channels and apply an Integral Radial Operation to emphasize salient radial features. Secondly, the Spatial Fourier Extraction operation is used for a spatial shift, utilizing a pre-trained deep learning network for optimal feature extraction. Finally, the deep neural network classification stage processes the data through dense layers using softmax for classification. Our approach significantly enhances the accuracy of differentiating between real and AI-generated images, as evidenced by a 12.64% increase in accuracy and 28.43% increase in AUC compared to existing state-of-the-art methods.
RevEx: An Online Consumer Reviews Extraction Tool
Authors: Julián Alarte, Carlos Galindo, Carlos Martín, Josep Silva
Venue: CIKM
Year: 2024

This paper presents RevEx, an online consumer reviews extraction tool. RevEx extracts the comments section for products in webshops. In contrast to other web scraping tools, it can work with heterogeneous web pages automatically, that is, it does not need any additional information apart from the web page itself. In addition, RevEx is a page-level tool since it only needs to load the web page whose comments have to be extracted. The technique includes a mechanism to group similar DOM nodes and then, once several sets of similar DOM nodes are obtained, an algorithm selects the group of DOM nodes that corresponds to the comments of the web page. The results of the empirical evaluation show an average F1 higher than 88%, and perfect results for around 75% of web pages.
Assessing Human Viewpoints in Theory of Mind for Large Language Models in Open-Ended Questioning
Authors: Maryam Amirizaniani
Venue: CIKM
Year: 2024

Theory of Mind (ToM) reasoning involves understanding that others have unique mental states-like beliefs, thoughts, intentions, viewpoints, and emotions-different from one's own, and incorporating this into one's reasoning. While some research suggests that LLMs possess reasoning abilities, other studies challenge this assertion, often focusing on structured responses and overlooking the complexities of open-ended interactions. As LLMs are increasingly employed in different sectors, their ability to accurately interpret human mental states in reasoning becomes critical. For example, in psychological services, if LLMs generate reasoning responses without understanding human mental states, their answers may lack logical soundness and potentially exacerbate client distress. Therefore, understanding LLMs' ToM capabilities is crucial to ensure they deliver effective and appropriate responses in real-world scenarios. In this research, I investigate the effectiveness of incorporating questioners' viewpoints in the questions-whether posed in a rational or intuitive manner-on the generation of reasoning answers by LLMs and how these generated answers align with human-written responses. The results demonstrate that incorporating these viewpoints into the prompt instructions enhances the reasoning performance of LLMs, although the responses still fall short of being truly human-like. This research contributes to the information retrieval and generative AI community by raising awareness about the limitations of LLMs in reasoning and their alignment with human responses in this domain.