OpenRaiser/Intern-Atlas huggingface dataset:
Deep Presentation Bias Integrated Framework for CTR Prediction
Authors: Jianqiang Huang, Xingyuan Tang, Zhe Wang, Shaolin Jia, Yin Bai, Zhiwei Liu, Jia Cheng, Jun Lei, Yan Zhang
Venue: CIKM
Year: 2022

In online advertising, click-through rate (CTR) prediction typically utilizes click data to train models for estimating the probability of a user clicking on an item. However, the different presentations of an item, including its position and contextual items, etc., will affect the user's attention and lead to different click propensities, thus the presentation bias arises. Most previous works generally consider position bias and pay less attention to overall presentation bias including context. Simultaneously, since the final presentation list is unreachable during online inference, the bias independence assumption is adopted so that the debiased relevance can be directly used for ranking. But this assumption is difficult to hold because the click propensity to the item presentation varies with user intent. Therefore, predicted CTR with personalized click propensity rather than debiased relevance should be closer to real CTR. In this work, we propose a Deep Presentation Bias Integrated Framework (DPBIF). With DPBIF, the presentation block containing item and contextual items on the same screen is introduced into user behavior sequence and predicted target item for personalizing the integration of presentation bias caused by different click propensities into CTR prediction network. While avoiding modeling with the independence assumption, the network is capable of estimating multiple integrated CTRs under different presentations for each item. The multiple CTRs are used to transform the ranking problem into an item-to-position assignment problem so that the Kuhn-Munkres (KM) algorithm is employed to optimize the global benefit of the presentation list. Extensive offline experiments and online A/B tests are performed in a real-world system to demonstrate the effectiveness of the proposed framework.
DuIVRS: A Telephonic Interactive Voice Response System for Large-Scale POI Attribute Acquisition at Baidu Maps
Authors: Jizhou Huang, Haifeng Wang, Shaolei Wang
Venue: CIKM
Year: 2022

The task of POI attribute acquisition, which aims at completing missing attributes (e.g., POI name, address, status, phone, and open/close time) for a point of interest (POI) or updating existing attribute values of a POI, plays an essential role in enabling users to entertain location-based services using commercial map applications, such as Baidu Maps. Existing solutions have adopted street views or web documents to acquire POI attributes, which have a major limitation in applying for large-scale production due to the labor-intensive and time-consuming nature of collecting data, error accumulation in processing textual/visual data in unstructured or free format, and necessitating post-processing steps with manual efforts. In this paper, we present our efforts and findings from a 3-year longitudinal study on designing and implementing DuIVRS, which is an alternative, fully automatic, and production-proven solution for large-scale POI attribute acquisition via completely machine-directed dialogues. Specifically, DuIVRS is designed to proactively acquire POI attributes via a telephonic interactive voice response system, whose tasks are to generate machine-initiative directed dialogues, make scripted telephone calls to businesses, and interact with people who answered the phone to achieve predefined goals through multi-turn dialogues. DuIVRS has already been deployed in production at Baidu Maps since December 2018, which greatly improves productivity and reduces production cost of POI attribute acquisition. As of December 31, 2021, DuIVRS has made 140 million calls and 42 million POI attribute updates within a 3-year period, which represents an approximately 3-year workload for a high-performance team of 1,000 call center workers. This demonstrates that DuIVRS is an industrial-grade and robust solution for cost-effective, large-scale acquisition of POI attributes.
Self-Supervised Learning for Recommendation
Authors: Chao Huang, Lianghao Xia, Xiang Wang, Xiangnan He, Dawei Yin
Venue: CIKM
Year: 2022

Recommender systems are playing an increasingly critical role to alleviate information overload and satisfy users' information seeking requirements in a wide spectrum of online platforms. However, the ubiquity of data sparsity and noise notably limits the representation capacity of existing recommender systems to learn high-quality user (item) embeddings. Inspired by recent advances of self-supervised learning (SSL) techniques, SSL-based representation learning models benefit a variety of recommendation domains. Such methods have achieved new levels of performance while reducing the dependence on observed supervision labels in diverse recommendation tasks. In this tutorial, we aim to provide a systemic review of state-of-the-art SSL-based recommender systems. To be specific, we summarize and categorize existing work of SSL-based recommender systems in terms of recommendation scenarios. For each type of recommendation task, the corresponding challenges and methods will be presented in a comprehensive way. Finally, some future directions and open questions will be raised to inspire more investigation on this important research line.
LGP: Few-Shot Class-Evolutionary Learning on Dynamic Graphs
Authors: Tiancheng Huang, Feng Zhao, Donglin Wang
Venue: CIKM
Year: 2022

Graph few-shot learning aims to learn how to quickly adapt to new tasks using only a few labeled data, which transfers learned knowledge of base classes to novel classes. Existing methods are mainly designed for static graphs, while many real-world graphs are dynamic and evolving over time, resulting in a phenomenon of structure and class evolutions. To address the challenges caused by the phenomenon, in this paper, we propose a novel algorithm named Learning to Generate Parameters (LGP) to deal with few-shot class-evolutionary learning on dynamic graphs. Specifically, for the structure evolution, LGP integrates ensemble learning into a backbone network to effectively learn invariant representation across different snapshots within a dynamic graph. For the class evolution, LGP adopts a meta-learning strategy that can learn to generate the classified parameters of novel classes via the parameters of the base classes. Therefore, LGP can quickly adapt to new tasks on a combination of base and novel classes. Besides, LGP utilizes an attention mechanism to capture the evolutionary pattern between the novel and based classes. Extensive experiments on a real-world dataset demonstrate the effectiveness of LGP.
An Uncertainty-Aware Imputation Framework for Alleviating the Sparsity Problem in Collaborative Filtering
Authors: Sunghyun Hwang, Dong-Kyu Chae
Venue: CIKM
Year: 2022

Collaborative Filtering (CF) methods for recommender systems commonly suffer from the data sparsity issue. Data imputation has been widely adopted to deal with this issue. However, existing studies have limitations in the sense that both uncertainty and robustness of imputation have not been taken into account, where there is a high risk that the imputed values are likely to be far from the true values. This paper explores a novel imputation framework, named Uncertainty-Aware Multiple Imputation (UA-MI), which can effectively solve the sparsity issue. Given a (sparse) user-item interaction matrix, our key idea is to quantify uncertainty on each missing entry and then the cells with the lowest uncertainty are selectively imputed. Here, we suggest three strategies for measuring uncertainty in missing user-item interactions, each of which is based on sampling, dropout, and ensemble, respectively. They successfully obtain element-wise mean and variance on the missing entries, where the variance helps determine where in the matrix should be imputed and the corresponding mean values are imputed. Experiments show that our UA-MI framework significantly outperformed the existing imputation strategies
Best Practices for Top-N Recommendation Evaluation: Candidate Set Sampling and Statistical Inference Techniques
Authors: Ngozi Ihemelandu
Venue: CIKM
Year: 2022

Top-N recommendation evaluation experiments are complex, with many decisions needed. These decisions are often made inconsistently, and we don't have clear best practices for many of them. The goal of this project, is to identify, substantiate, and document best practices to improve evaluations.
Incorporating Fairness in Large-scale Evacuation Planning
Authors: Kazi Ashik Islam, Da Qi Chen, Madhav V. Marathe, Henning S. Mortveit, Samarth Swarup, Anil Vullikanti
Venue: CIKM
Year: 2022

Evacuation planning is an essential part of disaster management where the goal is to relocate people in a safe and orderly manner. Existing research has shown that such problems are hard to approximate and current methods are difficult to scale to real-life applications. We introduce a notion of fairness and two related objectives while studying evacuation planning, namely: minimizing maximum inconvenience and minimizing average inconvenience. We show that both problems are not just NP-hard to solve exactly, but in fact are NP-hard to approximate. On the positive side, we present a heuristic optimization method MIP-LNS, based on the well-known Large Neighborhood Search framework, that can find good approximate solutions in reasonable amount of time. We also consider a multi-objective problem where the goal is to minimize both objectives and solve it using MIP-LNS. We use real-world road network and population data from Harris County in Houston, Texas (a region that needed large-scale evacuations in the past), and apply MIP-LNS to calculate evacuation plans for the area. We compare the quality of the plans in terms of evacuation efficiency and fairness. We find that the solutions to the multi-objective problem are superior in both of these aspects. We also perform statistical tests to show that the solutions are significantly different.
NILK: Entity Linking Dataset Targeting NIL-linking Cases
Authors: Anastasiia Iurshina, Jiaxin Pan, Rafika Boutalbi, Steffen Staab
Venue: CIKM
Year: 2022

The NIL-linking task in Entity Linking deals with cases where the text mentions do not have a corresponding entity in the associated knowledge base. NIL-linking has two sub-tasks: NIL-detection and NIL-disambiguation. NIL-detection identifies NIL-mentions in the text. Then, NIL-disambiguation determines if some NIL-mentions refer to the same out-of-knowledge base entity. Although multiple existing datasets can be adapted for NIL-detection, none of them address the problem of NIL-disambiguation. This paper presents NILK, a new dataset for NIL-linking processing, constructed from WikiData and Wikipedia dumps from two different timestamps. The NILK dataset has two main features: 1) It marks NIL-mentions for NIL-detection by extracting mentions which belong to newly added entities in Wikipedia text. 2) It provides an entity label for NIL-disambiguation by marking NIL-mentions with WikiData IDs from the newer dump. We make available the annotated dataset along with the code1. The NILK dataset is available at: https://zenodo.org/record/6607514
Discovering Fine-Grained Semantics in Knowledge Graph Relations
Authors: Nitisha Jain, Ralf Krestel
Venue: CIKM
Year: 2022

Knowledge graphs (KGs) provide structured representation of data in the form of relations between different entities. The semantics of relations between words and entities are often ambiguous, where it is common to find polysemous relations that represent multiple semantics based on the context. This ambiguity in relation semantics also proliferates KG triples. While the guidance from custom-designed ontologies addresses this issue to some extent, our analysis shows that the heterogeneity and complexity of real-world data still results in substantial relation polysemy within popular KGs. The correct semantic interpretation of KG relations is necessary for many downstream applications such as entity classification and question answering. We present the problem of fine-grained relation discovery and a data-driven method towards this task that leverages the vector representations of the knowledge graph entities and relations available from relational learning models. We show that by performing clustering over these vectors, our method is able to not only identify the polysemous relations in knowledge graphs, but also discover the different semantics associated with them. Extensive empirical evaluation shows that fine-grained relations discovered by the proposed approach lead to substantial improvement in the semantics in the Yago and NELL datasets, as compared to baselines. Additional insights from qualitative analyses convey that fine-grained relation discovery is an important yet complex task, especially in the presence of complex ontologies and noisy data.
RealGraphGPU: A High-Performance GPU-Based Graph Engine toward Large-Scale Real-World Network Analysis
Authors: Myung-Hwan Jang, Yun-Yong Ko, Dongkyu Jeong, Jeong-Min Park, Sang-Wook Kim
Venue: CIKM
Year: 2022

A graph, consisting of vertices and edges, has been widely adopted for network analysis. Recently, with the increasing size of real-world networks, many graph engines have been studied to efficiently process large-scale real-world graphs. RealGraph, one of the state-of-the-art single-machine-based graph engines, efficiently processes storage-to-memory I/Os by considering unique characteristics of real-world graphs. Via an in-depth analysis of RealGraph, however, we found that there is still a chance for more performance improvement in the computation part of RealGraph despite its great I/O processing ability. Motivated by this, in this paper, we propose RealGraphGPU, a GPU-based single-machine graph engine. We design the core components required for GPU-based graph processing and incorporate them into the architecture of RealGraph. Further, we propose two optimizations that successfully address the technical issues that could cause the performance degradation in the GPU-based graph engine: buffer pre-checking and edge-based workload allocation strategies. Through extensive evaluation with 6 real-world datasets, we demonstrate that (1) RealGraphGPU improves RealGraph by up to 546%, (2) RealGraphGPU outperforms existing state-of-the-art graph engines dramatically, and (3) the optimizations are all effective in large-scale graph processing.
Hockey: A Hybrid PMem-SSD Storage Engine for Analytical Database
Authors: Yuhang Jia, Huiqi Hu, Xuan Zhou, Weining Qian
Venue: CIKM
Year: 2022

Storage engines for analytic databases are being developed to be setup with different devices for both cost price and performance considerations. Persistent Memory(PMem), as a novel storage device, also provides a new promising option for the hybrid storage. In this demonstration, we introduce Hockey, an efficient columnar storage engine designed for hybrid PMem-SSD storage. We first go through the design of the system and introduce how the data and meta data are structured and accessed on the PMem. The system's data placement strategy on hybrid storage is then presented. To highlight Hockey's design concerns for the hybrid storage as well as its superior performance, we develop a visual interface to demonstrate the system through three scenarios.
Diverse Effective Relationship Exploration for Cooperative Multi-Agent Reinforcement Learning
Authors: Hao Jiang, Yuntao Liu, Shengze Li, Jieyuan Zhang, Xinhai Xu, Donghong Liu
Venue: CIKM
Year: 2022

In some complex multi-agent environments, the types of relationships between agents are diverse and their intensity changes during the policy learning process. Theoretically, some of these relationships can facilitate cooperative policy learning. However, acquiring these relationships is an intractable problem. To tackle the problem, we propose a diverse effective relationship exploration based multi-agent reinforcement learning (DERE) method. Specifically, a potential fields model is firstly designed to represent relationships between agents. Then to encourage the exploration of effective relationships, we define an information-theoretic objective function. Finally, an intrinsic reward function is designed to optimize the information-theoretic objective, meanwhile, guide agents to learn more effective collaborative policies. Experimental results show that our method outperforms state-of-the-art methods on both super hard StarCraft II micromanagement tasks (SMAC) and Google Research Football (GRF).
Adaptive Domain Interest Network for Multi-domain Recommendation
Authors: Yuchen Jiang, Qi Li, Han Zhu, Jinbei Yu, Jin Li, Ziru Xu, Huihui Dong, Bo Zheng
Venue: CIKM
Year: 2022

Industrial recommender systems usually hold data from multiple business scenarios and are expected to provide recommendation services for these scenarios simultaneously. In the retrieval step, the topK high-quality items selected from a large number of corpus usually need to be various for multiple scenarios. Take Alibaba display advertising system for example, not only because the behavior patterns of Taobao users are diverse, but also differentiated scenarios' bid prices assigned by advertisers vary significantly. Traditional methods either train models for each scenario separately, ignoring the cross-domain overlapping of user groups and items, or simply mix all samples and maintain a shared model which makes it difficult to capture significant diversities between scenarios. In this paper, we present Adaptive Domain Interest Network(ADIN) that adaptively handles the commonalities and diversities across scenarios, making full use of multi-scenarios data during training. Then the proposed method is able to improve the performance of each business domain by giving various topK candidates for different scenarios during online inference. Specifically, our proposed ADIN models the commonalities and diversities for different domains by shared networks and domain-specific networks, respectively. In addition, we apply the domain-specific batch normalization and design the domain interest adaptation layer for feature-level domain adaptation. A self training strategy is also incorporated to capture label-level connections across domains.ADIN has been deployed in the display advertising system of Alibaba, and obtains 1.8% improvement on advertising revenue.
Towards Federated Learning against Noisy Labels via Local Self-Regularization
Authors: Xuefeng Jiang, Sheng Sun, Yuwei Wang, Min Liu
Venue: CIKM
Year: 2022

Federated learning (FL) aims to learn joint knowledge from a large scale of decentralized devices with labeled data in a privacy-preserving manner. However, data with noisy labels are ubiquitous in reality since high-quality labeled data require expensive human efforts, which cause severe performance degradation. Although a lot of methods are proposed to directly deal with noisy labels, these methods either require excessive computation overhead or violate the privacy protection principle of FL. To this end, we focus on this issue in FL with the purpose of alleviating performance degradation yielded by noisy labels meanwhile guaranteeing data privacy. Specifically, we propose a Local Self-Regularization method, which effectively regularizes the local training process via implicitly hindering the model from memorizing noisy labels and explicitly narrowing the model output discrepancy between original and augmented instances using self distillation. Experimental results demonstrate that our proposed method can achieve notable resistance against noisy labels in various noise levels on three benchmark datasets. In addition, we integrate our method with existing state-of-the-arts and achieve superior performance on the real-world dataset Clothing1M.The code is available at https://github.com/Sprinter1999.
A Real-time Post-processing System for Itinerary Recommendation
Authors: Linge Jiang, Guiyang Wang, Zhibo Zhu, Binghao Wang, Runsheng Gan, Ziqi Liu, Jun Zhou
Venue: CIKM
Year: 2022

Post-processing is crucial to modern recommendation systems to achieve various purposes, e.g., improving diversity, and giving reasonable itineraries which consist of combinations of items, but is merely studied in the literature. We decouple the recommendation system into two modules including a reward estimation module and a post-processing module. Our real-time post-processing module built on Ray abstracts the common post-processing problems in the itinerary recommendation as combinatorial optimization problems. Under the goal of maximizing the click-through rate, the more reasonable recommendation results are obtained by imposing various constraints on the candidate items. However, the optimization problems are typically mixed integer programming problems with quadratic terms in practice, which are NP-hard. In real-time scenarios, there are extremely high requirements for the speed of the solving process. We speed up the problem solving by linearizing and relaxing the original problem and use Ray serving as the underlying service to provide stable and efficient technical support. At last, We provide services to users by deploying the post-processing module in the itinerary recommendation scenario at Alipay's built-in applet named ''What's nearby''. The online A/B experiment shows that the user exposure click rate can be significantly improved.
MCSCSet: A Specialist-annotated Dataset for Medical-domain Chinese Spelling Correction
Authors: Wangjie Jiang, Zhihao Ye, Zijing Ou, Ruihui Zhao, Jianguang Zheng, Yi Liu, Bang Liu, Siheng Li, Yujiu Yang, Yefeng Zheng
Venue: CIKM
Year: 2022

Chinese Spelling Correction (CSC) is gaining increasing attention in recent years. Despite its extensive use in many applications, such as search engine and optical character recognition system, little has been explored in medical scenarios in which complex and uncommon medical entities are easily misspelled. Correcting the misspellings of medical entities is arguably more difficult than those in the open domain due to its requirements of specific domain knowledge. In this work, we define the task of Medical-domain Chinese Spelling Correction (MCSC) and propose MCSCSet, a large-scale specialist-annotated dataset that contains about 200k samples. In contrast to existing open-domain CSC datasets, MCSCSet involves: i) extensive real-world medical queries collected from Tencent Yidian, ii) corresponding misspelled sentences manually annotated by medical specialists. Our work further offers a medical-domain confusion set consisting of the common error-prone characters in medicine and their corresponding misspellings. Extensive empirical studies have shown significant gaps between the open-domain and medical-domain spelling correction, highlighting the need to develop high-quality datasets that allow for CSC in specific domains. Moreover, our work benchmarks several representative methods, establishing baselines for future work.
AI-Augmented Art Psychotherapy through a Hierarchical Co-Attention Mechanism
Authors: Seungwan Jin, Hoyoung Choi, Kyungsik Han
Venue: CIKM
Year: 2022

One of the significant social problems emerging in modern society is mental illness, and a growing number of people are seeking psychological help. Art therapy is a technique that can alleviate psychological and emotional conflicts through creation. However, the expression of a drawing varies by individuals, and the subjective judgments made by art therapists raise the need to secure an objective assessment. In this paper, we present M2C (Multimodal classification with 2-stage Co-attention), a deep learning model that predicts stress from art therapy psychological test data. M2C employs a co-attention mechanism that combines two modalities-drawings and post-questionnaire answers-to complement the weaknesses of each, which corresponds to therapists' psychometric diagnostic processes. The results of the experiment show that M2C yielded higher performance than other state-of-the-art single- or multi-modal models, demonstrating the effectiveness of the co-attention approach that reflects the diagnosis process.