OpenRaiser/Intern-Atlas huggingface dataset:
A Data-Driven Index Recommendation System for Slow Queries
Authors: Gan Peng, Peng Cai, Kaikai Ye, Kai Li, Jinlong Cai, Yufeng Shen, Han Su
Venue: CIKM
Year: 2023

The Database Autonomy Service (DAS) is a platform designed to assist database administrators in managing a large number of database instances within major internet companies. One of the key tasks in DAS is to find missing indexes to improve the slow query execution. In Meituan, a vast array of business lines deploy tens of thousands of MySQL database instances. Consequently, a great number of human-generated index cases are accumulated in the DAS platform. This motivates us to build a data-driven index recommendation system, referred to as idxLearner, which can learn index creation knowledge from human-generated index cases. In this demonstration, users can interact with idxLearner by choosing source databases to construct the training data, training the recommendation model, inputting slow queries for various target databases, and observing the recommended indexes and their evaluation results.
Evaluating and Optimizing the Effectiveness of Neural Machine Translation in Supporting Code Retrieval Models: A Study on the CAT Benchmark
Authors: Hung Phan, Ali Jannesari
Venue: CIKM
Year: 2023

Neural Machine Translation (NMT) is widely applied in software engineering tasks. The effectiveness of NMT for code retrieval relies on the ability to learn from the sequence of tokens in the source language to the sequence of tokens in the target language. While NMT performs well in pseudocode-to-code translation[17], it might have challenges in learning to translate from natural language query to source code in newly curated real-world code documentation/ implementation datasets. In this work, we analyze the performance of NMT in natural language-to-code translation in the newly curated CAT benchmark[31] that includes the optimized versions of three Java datasets TLCodeSum, CodeSearchNet, Funcom, and a Python dataset PCSD. Our evaluation shows that NMT has low accuracy, measured by CrystalBLEU[10] and Meteor[9] metrics in this task. To alleviate the duty of NMT in learning complex representation of source code, we propose ASTTrans Representation, a tailored representation of an Abstract Syntax Tree (AST) using a subset of non-terminal nodes. We show that the classical approach NMT performs significantly better in learning ASTTrans Representation over code tokens with up to 36% improvement on Meteor score. Moreover, we leverage ASTTrans Representation to conduct combined code search processes from the state-of-the-art code search processes using GraphCodeBERT[13], and UniXcoder[12]. Our NMT models of learning ASTTrans Representation can boost the Mean Reciprocal Rank of these state-of-the-art code search processes by up to 3.08% and improve 23.08% of queries' results over the CAT benchmark.
Leveraging Graph Neural Networks for User Profiling: Recent Advances and Open Challenges
Authors: Erasmo Purificato, Ludovico Boratto, Ernesto William De Luca
Venue: CIKM
Year: 2023

The proposed tutorial aims to familiarise the CIKM community with modern user profiling techniques that utilise Graph Neural Networks (GNNs). Initially, we will delve into the foundational principles of user profiling and GNNs, accompanied by an overview of relevant literature. We will subsequently systematically examine cutting-edge GNN architectures specifically developed for user profiling, highlighting the typical data utilised in this context. Furthermore, ethical considerations and beyond-accuracy perspectives, e.g. fairness and explainability, will be discussed regarding the potential applications of GNNs in user profiling. During the hands-on session, participants will gain practical insights into constructing and training recent GNN models for user profiling using open-source tools and publicly available datasets. The audience will actively explore the impact of these models through case studies focused on bias analysis and explanations of user profiles. To conclude the tutorial, we will analyse existing and emerging challenges in the field and discuss future research directions.
IoTBDH-2023: The 5th International Workshop on Internet of Things of Big Data for Healthcare
Authors: Jun Qi, Hongqing Yu, Po Yang, Yun Yang, Zhibo Pang
Venue: CIKM
Year: 2023

Internet of Things (IoT) enabled technology has rapidly and efficiently facilitate healthcare diagnose and treatment with low-cost and lightweight devices. Big data generated from IoT offers valuable and crucial information to guide decision-making, improve patient outcomes, and decrease healthcare costs, etc. The workshop is aiming to provide an opportunity for researchers and practitioners from both academia and industry to present the state-of-the-art research and applications in utilizing IoT and big data technology for healthcare by presenting efficient scientific and engineering solutions, addressing the needs and challenges for integration with new technologies, and providing visions for future research and development.
International Workshop on Edge-Cloud Intelligence (ECI@CIKM2023)
Authors: Lianyong Qi, Xuyun Zhang, Xiaolong Xu, Wenwen Gong
Venue: CIKM
Year: 2023

With the exponential growth of mobile devices and the increasing demand for personalized and context-aware services, service-oriented applications play a crucial role in alleviating information overload and improving user experience. At the same time, Edge-Cloud Intelligence (ECI) provides a distributed computing paradigm that leverages the computational capabilities of edge servers to bring services closer to end-users, enabling low-latency and high-bandwidth data processing. The convergence of service applications and ECI has emerged as a promising way to enhance the efficiency and quality of service provisioning in today's dynamic and resource-constrained mobile environment. This call for papers invites researchers and practitioners to contribute to the exploration of the synergy between service applications and ECI infrastructure.
Bi-channel Multiple Sparse Graph Attention Networks for Session-based Recommendation
Authors: Shutong Qiao, Wei Zhou, Junhao Wen, Hongyu Zhang, Min Gao
Venue: CIKM
Year: 2023

Session-based Recommendation (SBR) has recently received significant attention due to its ability to provide personalized recommendations based on the interaction sequences of anonymous session users. The challenges facing SBR consist mainly of how to utilize information other than the current session and how to reduce the negative impact of irrelevant information in the session data on the prediction. To address these challenges, we propose a novel graph attention network-based model called Multiple Sparse Graph Attention Networks (MSGAT). MSGAT leverages two parallel channels to model intra-session and inter-session information. In the intra-session channel, we utilize a gated graph neural network to perform initial encoding, followed by a self-attention mechanism to generate the target representation. The global representation is then noise-reduced based on the target representation. Additionally, the target representation is used as a medium to connect the two channels. In the inter-session channel, the noise-reduced relation representation is generated using the global attention mechanism of target perception. Moreover, MSGAT fully considers session similarity from the intent perspective by integrating valid information from both channels. Finally, the intent neighbor collaboration module effectively combines relevant information to enhance the current session representation. Extensive experiments on five datasets demonstrate that simultaneous modeling of intra-session and inter-session data can effectively enhance the performance of the SBR model.
Enhancing Repeat-Aware Recommendation from a Temporal-Sequential Perspective
Authors: Shigang Quan, Shui Liu, Zhenzhe Zheng, Fan Wu
Venue: CIKM
Year: 2023

Repeat consumption, such as re-purchasing items and re-listening songs, is a common scenario in daily life. To model repeat consumption, the repeat-aware recommendation has been proposed to predict which item will be re-interacted based on the user-item interactions. In this paper, we investigate various inherent characteristics to enhance the performance of repeat-aware recommendation. Specifically, we explore these characteristics from two aspects: one is from the temporal aspect where we consider the time interval relationship in user behavior sequence; the other is from the sequential aspect where we consider the sequential-level relationship. Our intuition is that both thetemporal pattern andsequential pattern reflect users' intentions of repeat consumption. By utilizing these two patterns, a novel model called Temporal and Sequential repeat-aware Recommendation(TSRec for short) is proposed to enhance repeat-aware recommendation. TSRec has three main components: 1) User-specific Temporal Representation Module (UTRM), which encodes and extracts user historical repeat temporal information. 2) Item-specific Temporal Representation Module (ITRM), which incorporates item time interval information as side information to alleviate the data sparsity problem of user repeat behavior sequence. 3) Sequential Repeat-Aware Module (SRAM), which represents the similarity between user's current and the last repeat sequences. Extensive experimental results on three public benchmarks demonstrate the superiority of TSRec over state-of-the-art methods. The code is released online.
A Neuro-symbolic Approach to Enhance Interpretability of Graph Neural Network through the Integration of External Knowledge
Authors: Kislay Raj
Venue: CIKM
Year: 2023

Graph Neural Networks (GNNs) have shown remarkable performance in tackling complex tasks. However, interpreting the decision-making process of GNNs remains a challenge. To address the challenge, we explore representing the behaviour of a GNN in a representation space that is more transparent such as a knowledge graph, in a way that captures the behaviour of a GNN as a graph. Our initial experiments on the node classification task can represent the trained graph convolutional neural network (GCN) behaviour with some semantics uncovered by state-of-the-art approaches. This research offers a promising direction for enhancing GNN interpretability and understanding by providing structured, human-understandable representations and incorporating external knowledge for more accurate predictions.
Incremental Graph Classification by Class Prototype Construction and Augmentation
Authors: Yixin Ren, Li Ke, Dong Li, Hui Xue, Zhao Li, Shuigeng Zhou
Venue: CIKM
Year: 2023

Graph neural networks (GNNs) are prone to catastrophic forgetting of past experience in continuous learning scenarios. In this work, we propose a novel method for class-incremental graph learning (CGL) by class prototype construction and augmentation, which can effectively overcome catastrophic forgetting and requires no storage of exemplars (i.e., data-free). Concretely, on the one hand, we construct class prototypes in the embedding space that contain rich topological information of nodes or graphs to represent past data, which are then used for future learning. On the other hand, to boost the adaptability of the model to new classes, we employ class prototype augmentation (PA) to create virtual classes by combining current prototypes. Theoretically, we show that PA can promote the model's adaptation to new data and reduce the inconsistency of old prototypes in the embedding space, therefore further mitigate catastrophic forgetting. Extensive experiments on both node and graph classification datasets show that our method significantly outperforms the existing methods in reducing catastrophic forgetting, and beats the existing methods in most cases in terms of classification accuracy.
Dual-Process Graph Neural Network for Diversified Recommendation
Authors: Yuanyi Ren, Hang Ni, Yingxue Zhang, Xi Wang, Guojie Song, Dong Li, Jianye Hao
Venue: CIKM
Year: 2023

The recommender system is one of the most fundamental information services. A significant effort has been devoted to improving prediction accuracy, inevitably leading to the potential degradation of recommendation diversity. Moreover, individuals have different needs for diversity. To address these problems, diversity-enhanced approaches are proposed to modify the recommender models. However, these methods fail to break free from the relevance-oriented paradigm and are mostly haunted by sharply-declined accuracy and high computational costs. To tackle these challenges, we propose the Dual-Process Graph Neural Network (DPGNN), an efficient diversity-enhanced recommender system, resonating with the dual-process model of human cognition and the arousal theory of human interest. The first stage reduces the risk of suboptimal output during the training procedure, which helps to find a solution outside the relevance-oriented paradigm. Moreover, the second stage utilizes user-specific rating adjustments, boosting the recommendation diversity and accommodating users' distinctive needs with minimum computational costs. Extensive experiments on real-world datasets verify the effectiveness of our method in improving diversity, while maintaining accuracy with low computational costs.
Dual-view Contrastive Learning for Auction Recommendation
Authors: Dan Ni Ren, Leong Hou U, Wei Liu
Venue: CIKM
Year: 2023

Recommendation systems in auction platforms like eBay function differently in comparison to those found in traditional trading platforms. The bidding process involves multiple users competing for a product, with the highest bidder winning the item. As a result, each transaction is independent and characterized by varying transaction prices. The individual nature of auction items means that users cannot purchase identical items, adding to the uniqueness of the purchasing history. Bidders in auction systems rely on their judgment to determine the value of a product, as bidding prices reflect preferences rather than cost-free actions like clicking or collecting. Conventional methodologies that heavily rely on user-item purchase history are ill-suited to handle these unique and extreme product features. Unfortunately, prior recommendation approaches have failed to give due attention to the contextual intricacies of auction items, thereby missing out on the full potential of the invaluable bidding record at hand. This paper introduces a novel contrastive learning approach for auction recommendation, addressing the challenges of data sparsity and uniqueness in auction recommendation. Our method focuses on capturing multiple behavior relations and item context through contrastive pairs construction, contrastive embedding, and contrastive optimization techniques from both user and item perspectives. By overcoming the limitations of previous approaches, our method delivers promising results on two auction datasets, highlighting the practicality and effectiveness of our model.
GBTTE: Graph Attention Network Based Bus Travel Time Estimation
Authors: Yuecheng Rong, Juntao Yao, Jun Liu, Yifan Fang, Wei Luo, Hao Liu, Jie Ma, Zepeng Dan, Jinzhu Lin, Zhi Wu, Yan Zhang, Chuanming Zhang
Venue: CIKM
Year: 2023

Real-time bus travel time is crucial for the smart public transportation system and is beneficial for improving user satisfaction for online map services. However, it faces great challenges due to fine-grained spatial dependencies and dynamic temporal dependencies. To address the above problem, we propose GBTTE, a novel end-to-end graph attention network framework to estimate bus travel time. Specifically, we construct a novel graph structure of bus routes and use a graph attention network to capture the fine-grained spatial features of bus routes. Then, we fully exploit the joint spatial-temporal relations of bus stops through a spatial-temporal graph attention network and also capture the dynamic correlation between the route and the bus transportation network with a cross graph attention network. Finally, we integrate the route representation, the spatial-temporal representation and contextual information to estimate bus travel time. Extensive experiments carried out on two large-scale real-world datasets demonstrate the effectiveness of GBTTE. In addition, GBTTE has been deployed in production at Baidu Maps, handling tens of millions of requests every day.
Deep Query Rewriting For Geocoding
Authors: Pravakar Roy, Chirag Sharma, Chao Gao, Kumarswamy Valegerepura
Venue: CIKM
Year: 2023

Query rewriting aims to bridge the gap between user queries and indexed data. In the context of geocoding, it augments the user query to match all relevant entities in indexed data. This work presents an end-to-end trainable attention-based query rewriting model for geocoding. Most recent works attempt to solve query rewriting with sequence-to-sequence models, which often fail to satisfy the tight latency constraints of production environments. Toward developing a low latency model, we formulate it as a combination of multiple token classification tasks. We introduce numerous novel techniques, such as constructing a separate output space for alterations containing whole words to tackle many-to-one/one-to-many associations, using regular expression rules as prediction classes to handle the limitations of having a non-word piece prediction space, and curbing the output space for each wordpiece in our vocabulary to reduce inference time by 20x in average. We also present a data generation pipeline that generates train/test data without human judgment from the query logs. Through offline and online experiments, we demonstrate that the proposed model is orders of magnitude faster than their seq2seq and SMT-based counterparts in inference time, achieves better/on-par performance, and improves the relevance of overall geocoding.
A Self-Learning Resource-Efficient Re-Ranking Method for Clinical Trials Search
Authors: Maciej Rybinski, Vincent Nguyen, Sarvnaz Karimi
Venue: CIKM
Year: 2023

Complex search scenarios, such as those in biomedical settings, can be challenging. One such scenario is matching a patient's profile to relevant clinical trials. There are multiple criteria that should match for a document (clinical trial) to be considered relevant to a query (patient's profile represented with an admission note). While different neural ranking methods have been proposed for searching clinical trials, resource-efficient approaches to ranker training are less studied. A resource-efficient method uses training data in moderation. We propose a self-learning reranking method that achieves results comparable to those of more complicated, fully supervised, systems. Our experiments demonstrate our method's robustness and competitiveness compared to the state-of-the-art approaches in clinical trial search.
Adversarial Density Ratio Estimation for Change Point Detection
Authors: Shreyas S, Prakash Mandayam Comar, Sivaramakrishnan Kaveri
Venue: CIKM
Year: 2023

Change Point Detection (CPD) models are used to identify abrupt changes in the distribution of a data stream and have a widespread practical use. CPD methods generally compare the distribution of data sequences before and after a given time step to infer if there is a shift in distribution at the said time step. Numerous divergence measures, which measure distance between data distributions of sequence pairs, have been proposed for CPD \citeMStatisticNIPS, BergCPD and often the choice of divergence measure depends on the data used. Density Ratio Estimation (DRE) \citeRelDivCPD,BergCPD can be used to estimate a broad family of f-divergences, which includes widely used CPD divergences like Kullback-Leibler (KL) and Pearson, and thus DRE is a popular approach for CPD. In this work, we improve upon the existing DRE techniques for CPD, by proposing a novel objective that combines DRE seamlessly with adversarial sample generation. The adversarial samples allows for a robust CPD with DRE to track subtle changes in distribution, leading to a reduction in false negatives. We experiment on a wide variety of real-world, public benchmark datasets to show that our approach improves upon existing state-of-the-art (SoTA) methods, including DRE based CPD methods, by demonstrating an \sim 5% increase in F-score.
Seq-HyGAN: Sequence Classification via Hypergraph Attention Network
Authors: Khaled Mohammed Saifuddin, Corey May, Farhan Tanvir, Muhammad Ifte Khairul Islam, Esra Akbas
Venue: CIKM
Year: 2023

Extracting meaningful features from sequences and devising effective similarity measures are vital for sequence data mining tasks, particularly sequence classification. While neural network models are commonly used to automatically learn sequence features, they are limited to capturing adjacent structural connection information and ignoring global, higher-order information between the sequences. To address these challenges, we propose a novel Hypergraph Attention Network model, namely Seq-HyGAN for sequence classification problems. To capture the complex structural similarity between sequence data, we create a novel hypergraph model by defining higher-order relations between subsequences extracted from sequences. Subsequently, we introduce a Sequence Hypergraph Attention Network that learns sequence features by considering the significance of subsequences and sequences to one another. Through extensive experiments, we demonstrate the effectiveness of our proposed Seq-HyGAN model in accurately classifying sequence data, outperforming several state-of-the-art methods by a significant margin.
Quantitative Decomposition of Prediction Errors Revealing Multi-Cause Impacts: An Insightful Framework for MLOps
Authors: Keita Sakuma, Ryuta Matsuno, Yoshio Kameda
Venue: CIKM
Year: 2023

As machine learning applications expand in various industries, MLOps, which enables continuous model operation and improvement, becomes increasingly significant. Identifying causes of prediction errors, such as low model performance or anomalous samples, and implementing appropriate countermeasures are essential for effective MLOps. Furthermore, quantitatively evaluating each cause's impact is necessary to determine the effectiveness of countermeasures. In this study, we propose a method to quantitatively decompose a single sample's prediction error into contributions from multiple causes. Our method involves four steps: calculating the prediction error, computing metrics related to error causes, using a regression model to learn the relationship between the error and metrics, and applying SHAP to interpret the model's predictions and calculate the contribution of each cause to the prediction error. Numerical experiments with open data show that our method offers valuable insights for model improvement, confirming the effectiveness of our approach.
Neural Disentanglement of Query Difficulty and Semantics
Authors: Sara Salamat, Negar Arabzadeh, Shirin Seyedsalehi, Amin Bigdeli, Morteza Zihayat, Ebrahim Bagheri
Venue: CIKM
Year: 2023

Researchers have shown that the retrieval effectiveness of queries may depend on other factors in addition to the semantics of the query. In other words, several queries expressed with the same intent, and even using overlapping keywords, may exhibit completely different degrees of retrieval effectiveness. As such, the objective of our work in this paper is to propose a neural disentanglement method that is able to disentangle query semantics from query difficulty. The disentangled query semantics representation provides the means to determine semantic association between queries whereas the disentangled query difficulty representation would allow for the estimation of query effectiveness. We show through our experiments on the query performance prediction; and, query similarity calculation tasks that our proposed disentanglement method is able to show better performance compared to the state of the art.