OpenRaiser/Intern-Atlas huggingface dataset:
James ate 5 oranges = Steve bought 5 pencils: Structure-Aware Denoising for Paraphrasing Word Problems
Authors: Rishabh Gupta, Venktesh V, Mukesh K. Mohania, Vikram Goyal
Venue: CIKM
Year: 2023

We propose SCANING, an unsupervised framework for paraphrasing via controlled noise injection. We focus on the novel task of paraphrasing algebraic word problems having practical applications in online pedagogy as a means to reduce plagiarism as well as evoke reasoning capabilities on the part of the student instead of rote memorization. This task is more complex than paraphrasing general-domain corpora due to the difficulty in preserving critical information for solution consistency of the paraphrased word problem, managing the increased length of the text and ensuring diversity in the generated paraphrase. Existing approaches fail to demonstrate adequate performance on at least one, if not all, of these facets, necessitating the need for a more comprehensive solution. To this end, we model the noising search space as a composition of contextual and syntactic aspects to sample noising functions. This allows for learning a denoising function, that operates over both aspects and produces semantically equivalent and syntactically diverse outputs through grounded noise injection. The denoising function serves as a foundation for training a paraphrasing function, which operates solely in the input-paraphrase space without carrying any direct dependency on noise. We demonstrate that SCANING improves performance in terms of producing semantically equivalent and syntactically diverse paraphrases by 35% through extensive automated and human evaluation across 4 datasets.
ELTRA: An Embedding Method based on Learning-to-Rank to Preserve Asymmetric Information in Directed Graphs
Authors: Masoud Reyhani Hamedani, Jin-Su Ryu, Sang-Wook Kim
Venue: CIKM
Year: 2023

Double-vector embedding methods capture the asymmetric information in directed graphs first, and then preserve them in the embedding space by providingtwo latent vectors, i.e., source and target, per node. Although these methods are known to besuperior to the single-vector ones (i.e., providing asingle latent vector per node), wepoint out their three drawbacks as inability to preserve asymmetry on NU-paths, inability to preserve global nodes similarity, and impairing in/out-degree distributions. To address these, we first proposeCRW, anovel similarity measure for graphs that considers contributions ofboth in-links and out-links in similarity computation,without ignoring their directions. Then, we proposeELTRA, aneffective double-vector embedding method to preserve asymmetric information in directed graphs. ELTRA computesasymmetry preserving proximity scores (AP-scores) by employing CRW in which the contribution of out-links and in-links in similarity computation isupgraded anddowngraded, respectively. Then, for every node u, ELTRA selects its top-tclosest nodes based on AP-scores andconforms theranks of their corresponding target vectors w.r.t u's source vector in the embedding space to theiroriginal ranks. Our extensive experimental results withseven real-world datasets andsixteen embedding methods show that (1) CRWsignificantly outperforms Katz and RWR in computing nodes similarity in graphs, (2) ELTRAoutperforms the existing state-of-the-art methods in graph reconstruction, link prediction, and node classification tasks.
Robust Basket Recommendation via Noise-tolerated Graph Contrastive Learning
Authors: Xinrui He, Tianxin Wei, Jingrui He
Venue: CIKM
Year: 2023

The growth of e-commerce has seen a surge in popularity of platforms like Amazon, eBay, and Taobao. This has given rise to a unique shopping behavior involving baskets - sets of items purchased together. As a less studied interaction mode in the community, the question of how should shopping basket complement personalized recommendation systems remains under-explored. While previous attempts focused on jointly modeling user purchases and baskets, the distinct semantic nature of these elements can introduce noise when directly integrated. This noise negatively impacts the model's performance, further exacerbated by significant noise (e.g., a user is misled to click an item or recognizes it as uninteresting after consuming it) within both user and basket behaviors. In order to cope with the above difficulties, we propose a novel Basket recommendation framework via Noise-tolerated Contrastive Learning, named BNCL, to handle the noise existing in the cross-behavior integration and within-behavior modeling. First, we represent the basket-item interactions as the hypergraph to model the complex basket behavior, where all items appearing in the same basket are treated as a single hyperedge. Second, cross-behavior contrastive learning is designed to suppress the noise during the fusion of diverse behaviors. Next, to further inhibit the within-behavior noise of the user and basket interactions, we propose to exploit invariant properties of the recommenders w.r.t augmentations through within-behavior contrastive learning. A novel consistency-aware augmentation approach is further designed to better identify the noisy interactions with the consideration of the above two types of interactions. Our framework BNCL offers a generic training paradigm that is applicable to different backbones. Extensive experiments on three shopping transaction datasets verify the effectiveness of our proposed method.
Large Language Models as Zero-Shot Conversational Recommenders
Authors: Zhankui He, Zhouhang Xie, Rahul Jha, Harald Steck, Dawen Liang, Yesu Feng, Bodhisattwa Prasad Majumder, Nathan Kallus, Julian J. McAuley
Venue: CIKM
Year: 2023

In this paper, we present empirical studies on conversational recommendation tasks using representative large language models in a zero-shot setting with three primary contributions. (1) Data: To gain insights into model behavior in "in-the-wild" conversational recommendation scenarios, we construct a new dataset of recommendation-related conversations by scraping a popular discussion website. This is the largest public real-world conversational recommendation dataset to date. (2) Evaluation: On the new dataset and two existing conversational recommendation datasets, we observe that even without fine-tuning, large language models can outperform existing fine-tuned conversational recommendation models. (3) Analysis: We propose various probing tasks to investigate the mechanisms behind the remarkable performance of large language models in conversational recommendation. We analyze both the large language models' behaviors and the characteristics of the datasets, providing a holistic understanding of the models' effectiveness, limitations and suggesting directions for the design of future conversational recommenders.
KGrEaT: A Framework to Evaluate Knowledge Graphs via Downstream Tasks
Authors: Nicolas Heist, Sven Hertling, Heiko Paulheim
Venue: CIKM
Year: 2023

In recent years, countless research papers have addressed the topics of knowledge graph creation, extension, or completion in order to create knowledge graphs that are larger, more correct, or more diverse. This research is typically motivated by the argumentation that using such enhanced knowledge graphs to solve downstream tasks will improve performance. Nonetheless, this is hardly ever evaluated. Instead, the predominant evaluation metrics - aiming at correctness and completeness - are undoubtedly valuable but fail to capture the complete picture, i.e., how useful the created or enhanced knowledge graph actually is. Further, the accessibility of such a knowledge graph is rarely considered (e.g., whether it contains expressive labels, descriptions, and sufficient context information to link textual mentions to the entities of the knowledge graph). To better judge how well knowledge graphs perform on actual tasks, we present KGrEaT - a framework to estimate the quality of knowledge graphs via actual downstream tasks like classification, clustering, or recommendation. Instead of comparing different methods of processing knowledge graphs with respect to a single task, the purpose of KGrEaT is to compare various knowledge graphs as such by evaluating them on a fixed task setup. The framework takes a knowledge graph as input, automatically maps it to the datasets to be evaluated on, and computes performance metrics for the defined tasks. It is built in a modular way to be easily extendable with additional tasks and datasets.
AutoBuild: Automatic Community Building Labeling for Last-mile Delivery
Authors: Zhiqing Hong, Dongjiang Cao, Haotian Wang, Guang Wang, Tian He, Desheng Zhang
Venue: CIKM
Year: 2023

Fine-grained community-building information, such as building names and accurate geographical coordinates, is critical for a range of practical applications like navigation and door-to-door services (e.g., on-demand delivery and last-mile delivery). A common practice of traditional methods to gather community-building information usually relies on manual collection, which is typically labor-intensive and time-consuming. To address these issues, we utilize the massive data generated from e-commerce delivery services and design a framework, AutoBuild, for fine-grained large-scale community-building labeling. AutoBuild consists of two main components: (i) a Location Candidate Detection Module that identifies potential building names and coordinates from multi-source delivery data, and (ii) a Progressive Building Matching Model that employs trajectory modeling, human behavior analysis, and heterogeneous graph alignment to match building names and coordinates. To evaluate the performance of AutoBuild, we applied it to two real-world multi-modal datasets from Beijing City and Chengdu City. The results reveal that AutoBuild significantly outperforms multiple baseline models by 50-meter accuracy of 81.8% and 100-meter accuracy of 95.9% in Beijing City. More importantly, we conduct a real-world case study to demonstrate the practical impact of AutoBuild in last-mile delivery.
Search-Efficient Computerized Adaptive Testing
Authors: Yuting Hong, Shiwei Tong, Wei Huang, Yan Zhuang, Qi Liu, Enhong Chen, Xin Li, Yuanjing He
Venue: CIKM
Year: 2023

Computerized Adaptive Testing (CAT) arises as a promising personalized test mode in online education, targeting at revealing students' latent knowledge state by selecting test items adaptively. The item selection strategy is the core component of CAT, which searches for the best suitable test item based on students' current estimated ability at each test step. However, existing selection strategies behave in a brute-force manner, which results in the time complexity being linear to the number of items (N) in the item pool, i.e., O(N). Thus, in reality, the search latency becomes the bottleneck for CAT with a large-scale item pool. To this end, we propose a Search-Efficient Computerized Adaptive Testing framework (SECAT), which aims at enhancing CAT with an efficient selection strategy. Specifically, SECAT contains two main phases: item pool indexing and item search. In the item pool indexing phase, we apply a student-aware spatial partition method on the item pool to divide the test items into many sub-spaces, considering the adaptability of test items. In the item search phase, we optimize the traditional single-round search strategy with the asymptotic theory and propose a multi-round search strategy that can further improve the time efficiency. Compared with existing strategies, the time complexity of SECAT decreases from O(N) to O(logN). Across two real-world datasets, SECAT achieves over 200x speed up with negligible accuracy degradation.
SANN: Programming Code Representation Using Attention Neural Network with Optimized Subtree Extraction
Authors: Muntasir Hoq, Sushanth Reddy Chilla, Melika Ahmadi Ranjbar, Peter Brusilovsky, Bita Akram
Venue: CIKM
Year: 2023

Automated analysis of programming data using code representation methods offers valuable services for programmers, from code completion to clone detection to bug detection. Recent studies show the effectiveness of Abstract Syntax Trees (AST), pre-trained Transformer-based models, and graph-based embeddings in programming code representation. However, pre-trained large language models lack interpretability, while other embedding-based approaches struggle with extracting important information from large ASTs. This study proposes a novel Subtree-based Attention Neural Network (SANN) to address these gaps by integrating different components: an optimized sequential subtree extraction process using Genetic algorithm optimization, a two-way embedding approach, and an attention network. We investigate the effectiveness of SANN by applying it to two different tasks: program correctness prediction and algorithm detection on two educational datasets containing both small and large-scale code snippets written in Java and C, respectively. The experimental results show SANN's competitive performance against baseline models from the literature, including code2vec, ASTNN, TBCNN, CodeBERT, GPT-2, and MVG, regarding accurate predictive power. Finally, a case study is presented to show the interpretability of our model prediction and its application for an important human-centered computing application, student modeling. Our results indicate the effectiveness of the SANN model in capturing important syntactic and semantic information from students' code, allowing the construction of accurate student models, which serve as the foundation for generating adaptive instructional support such as individualized hints and feedback.
SEDAR: A Semantic Data Reservoir for Heterogeneous Datasets
Authors: Sayed Hoseini, Ahmed Ali, Haron Shaker, Christoph Quix
Venue: CIKM
Year: 2023

Data lakes have emerged as a solution for managing vast and diverse datasets for modern data analytics. To prevent them from becoming ungoverned, semantic data management techniques are crucial, which involve connecting metadata with knowledge graphs, following the principles of Linked Data. This semantic layer enables more expressive data management, integration from various sources and enhances data access utilizing the concepts and relations to semantically enrich the data. Some frameworks have been proposed, but requirements like data versioning, linking of datasets, managing machine learning projects, automated semantic modeling and ontology-based data access are not supported in one uniform system. We demonstrate SEDAR, a comprehensive semantic data lake that includes support for data ingestion, storage, processing, and governance with a special focus on semantic data management. The demo will showcase how the system allows for various ingestion scenarios, metadata enrichment, data source linking, profiling, semantic modeling, data integration and processing inside a machine learning life cycle.
PS-SA: An Efficient Self-Attention via Progressive Sampling for User Behavior Sequence Modeling
Authors: Jiacen Hu, Zhangming Chan, Yu Zhang, Shuguang Han, Siyuan Lou, Baolin Liu, Han Zhu, Yuning Jiang, Jian Xu, Bo Zheng
Venue: CIKM
Year: 2023

As the self-attention mechanism offers powerful capabilities for capturing sequential relationships, it has become increasingly popular to use it for modeling user behavior sequences in recommender systems. However, the self-attention mechanism has a quadratic computational complexity of O(n^2), as it conducts interactions among all item pairs in the sequence. This can lead to expensive model training and slow inference speeds, which may hinder practical deployment. To this end, we pursue to develop alternative approaches to improve the efficiency of the self-attention mechanism. We observe that the attention scores calculated from each item interacting with other items (including itself) are sparse, indicating that there are limited valuable item pairs (with non-zero attention weight) that contribute to the final output. This motivates us to develop effective strategies for discerning valuable items and computing attention scores solely for these items, thereby minimizing the consumption of unnecessary computations. Herein, we present a novel Progressive Sampling-based Self-Attention (PS-SA) mechanism, which utilizes a learnable progressive sampling strategy to identify the most valuable items. Subsequently, we solely utilize these selected items to produce the final output. Experiments on academic and production datasets demonstrate PS-SA could still achieve promising results while reducing computational costs. It is notable that we have successfully deployed it on Alibaba display advertising system, resulting in a 2.6% CTR and 1.3% RPM increase.
Liberate Pseudo Labels from Over-Dependence: Label Information Migration on Sparsely Labeled Graphs
Authors: Zhihui Hu, Yao Fu, Hong Zhao, Xiaoyu Cai, Weihao Jiang, Shiliang Pu
Venue: CIKM
Year: 2023

Graph Convolutional Networks (GCNs) have made outstanding achievements in many tasks on graphs in recent years, but their success relies on sufficient training data. In practice, sparsely labeled graphs widely exist in the real world so self-training methods have become popular approaches by adding pseudo labeled nodes to enhance the performance of GCNs. However, we observe that most selected high-confidence pseudo labeled nodes by the existing methods would surround the true labeled nodes. It is what we called pseudo label over-dependence, which could lead to the non-uniform pseudo label distribution. Furthermore, a thorough experiment shows that the classification accuracy changes significantly under different label densities and the label-sparse regions show great potential improvement in the model performance. Based on the above findings, we theoretically analyze the constraint factors in the label-sparse regions and further propose reducing the feature distribution difference between the label-dense regions and label-sparse regions can effectively decrease the classification error. Thus, in this paper, we propose a novel Graph Label Information Migration framework (GLIM) to liberate pseudo labels from over-dependence. Specifically, we first propose a training dynamics module (TDM) that uses abundant training process information to find more reliable node labels and improve the model robustness against label noise. Then we propose a label migration module (LMM) that migrates label information from label-dense regions to label-sparse regions by a spectral based graph matching algorithm. These migrated labels are like the glimmers in the darkness, providing the supervision signals for the unlabeled nodes in label-sparse regions. Finally, we conduct extensive experiments to demonstrate the effectiveness of the proposed GLIM.
Enhanced Template-Free Reaction Prediction with Molecular Graphs and Sequence-based Data Augmentation
Authors: Haozhe Hu, Yongquan Jiang, Yan Yang, Jim X. Chen
Venue: CIKM
Year: 2023

Retrosynthesis and forward synthesis prediction are fundamental challenges in organic synthesis, computer-aided synthesis planning (CASP), and computer-aided drug design (CADD). The objective is to predict plausible reactants for a given target product and its corresponding inverse task. With the rapid development of deep learning, numerous approaches have been proposed to solve this problem from various perspectives. The methods based on molecular graphs benefit from their rich features embedded inside but face difficulties in applying existing sequence-based data augmentations due to the permutation invariance of graph structures. In this work, we propose SeqAGraph, a template-free approach that annotates input graphs with its root atom index to ensure compatibility with sequence-based data augmentation. The matrix product for global attention in graph encoders is implemented by indexing, elementwise product, and aggregation to fuse global attention with local message passing without graph padding. Experiments demonstrate that SeqAGraph fully benefits from molecular graphs and sequence-based data augmentation and achieves state-of-the-art accuracy in template-free approaches.
Celebrity-aware Graph Contrastive Learning Framework for Social Recommendation
Authors: Zheng Hu, Satoshi Nakagawa, Liang Luo, Yu Gu, Fuji Ren
Venue: CIKM
Year: 2023

Social networks exhibit a distinct "celebrity effect" whereby influential individuals have a more significant impact on others compared to ordinary individuals, unlike other network structures such as citation networks and knowledge graphs. Despite its common occurrence in social networks, the celebrity effect is frequently overlooked by existing social recommendation methods when modeling social relationships, thereby hindering the full exploitation of social networks to mine similarities between users. In this paper, we fill this gap and propose a Celebrity-aware Graph Contrastive Learning Framework for Social Recommendation (CGCL), which explicitly models the celebrity effect in the social domain. Technically, we measure the different influences of celebrity and ordinary nodes by mining social network structure features, such as closeness centrality. To model the celebrity effect in social networks, we design a novel user-user impact-aware aggregation method, which incorporates the celebrity-aware influence information into the message propagation process. Additionally, we design a graph neural network-based framework which incorporates social semantics into the user-item interaction modeling with contrastive learning-enhanced data augmentation. The experimental results on three real-world datasets show the effectiveness of the proposed framework. We conduct ablation experiments to prove that the key components of our model benefit the recommendation performance improvement.
STAMINA (Spatial-Temporal Aligned Meteorological INformation Attention) and FPL (Focal Precip Loss): Advancements in Precipitation Nowcasting for Heavy Rainfall Events
Authors: Ping-Chia Huang, Yueh-Li Chen, Yi-Syuan Liou, Bing-Chen Tsai, Chun-Chieh Wu, Winston H. Hsu
Venue: CIKM
Year: 2023

Precipitation nowcasting is crucial for weather-dependent decision-making in various sectors, providing accurate and high-resolution predictions of precipitation within a typical two-hour timeframe. Deep learning techniques have shown promise in improving nowcasting accuracy by leveraging large radar datasets. However, accurately predicting heavy rainfall events remains challenging due to several persistent problems in previous work. These include spatial-temporal misalignment between meteorological information and precipitation data, as well as the performance gap between different rainfall levels. To address these challenges, we propose two innovative modules: Spatial-Temporal Aligned Meteorological INformation Attention (STAMINA) and Focal Precip Loss (FPL). STAMINA integrates meteorological information using spatial-temporal embedding and pixelwise linear attention mechanisms to overcome spatial-temporal misalignment. FPL addresses event imbalance through event weighting and a penalty mechanism. Through extensive experiments, we demonstrate significant performance improvements achieved by STAMINA and FPL, with an 8% improvement in predicting light rainfall and, more significantly, a 30% improvement in heavy rainfall compared to the state-of-the-art DGMR model. These modules offer practical and effective solutions for enhancing nowcasting accuracy, with a specific focus on improving predictions for heavy rainfall events. By tackling the persistent problems in previous work, our proposed approach represents a significant advancement in the field of precipitation nowcasting.