OpenRaiser/Intern-Atlas huggingface dataset:
Jim: Joint influence modeling for collective search behavior
Authors: Shubhra Kanti Karmaker Santu, Liangda Li, Yi Chang, ChengXiang Zhai
Venue: Conference CIKM 2018
Year: 2018

Everyone in the class liked Jim. It’s hard to explain why, but there was something special about him that you couldn’t exactly put your finger on. His appearance was average in just about every way except maybe the sparkle in his big brown eyes, the hint of a smile that would appear at the slightest provocation, and the air of quiet confidence he projected by his body language. He was older than the rest of us by two or three years – maybe more. He already had a wife who was from Iran, Lila, and a little girl who we had all gotten to know, Sandy.
Robust dynamic clustering for temporal networks
Authors: Jingyi You, Chenlong Hu, Hidetaka Kamigaito, Kotaro Funakoshi, Manabu Okumura
Venue: Conference CIKM 2021
Year: 2021

Dynamic community detection (or graph clustering) in temporal networks has attracted much attention because it is promising for revealing the underlying mechanism of complex real-world systems. Current methods are criticized for the independence of graph representation learning and graph clustering, considerable noise during temporal information smoothing, and high time complexity. We propose a R obust T emporal S moothing C lustering method (RTSC), which involves joint graph representation learning and graph clustering, to solve these problems. RTSC can be formulated as a constrained multi-objective optimization problem. Specifically, three-order successive snapshots are first projected into the same subspace via graph embedding. We then use the embedding matrices to learn a common low-rank block-diagonal matrix that contains current clustering information and specific noise matrices with a sparse constraint to remove noise at each time step. To efficiently solve the challenging optimization problem, we also propose an optimization procedure based on the augmented Lagrangian multiplier (ALM) scheme. Experimental results on six artificial datasets and four real-world dynamic network datasets indicate that RTSC performs better than six state-of-the-art algorithms for dynamic clustering in temporal networks.
Caves: A dataset to facilitate explainable classification and summarization of concerns towards covid vaccines
Authors: Soham Poddar, Azlaan Mustafa Samad, Rajdeep Mukherjee, Niloy Ganguly, Saptarshi Ghosh
Venue: Conference SIGIR 2022
Year: 2022

Convincing people to get vaccinated against COVID-19 is a key societal challenge in the present times. As a first step towards this goal, many prior works have relied on social media analysis to understand the specific concerns that people have towards these vaccines, such as potential side-effects, ineffectiveness, political factors, and so on. Though there are datasets that broadly classify social media posts into Anti-vax and Pro-Vax labels, there is no dataset (to our knowledge) that labels social media posts according to the specific anti-vaccine concerns mentioned in the posts. In this paper, we have curated CAVES, the first large-scale dataset containing about 10k COVID-19 anti-vaccine tweets labelled into various specific anti-vaccine concerns in a multi-label setting. This is also the first multi-label classification dataset that provides explanations for each of the labels. Additionally, the dataset also provides class-wise summaries of all the tweets. We also perform preliminary experiments on the dataset and show that this is a very challenging dataset for multi-label explainable classification and tweet summarization, as is evident by the moderate scores achieved by some state-of-the-art models. Our dataset and codes are available at: https://github.com/sohampoddar26/caves-data
Task-adaptive neural process for user cold-start recommendation
Authors: Xixun Lin, Jia Wu, Chuan Zhou, Shirui Pan, Yanan Cao, Bin Wang
Venue: Conference WWW 2021
Year: 2021

User cold-start recommendation is a long-standing challenge for recommender systems due to the fact that only a few interactions of cold-start users can be exploited. Recent studies seek to address this challenge from the perspective of meta learning, and most of them follow a manner of parameter initialization, where the model parameters can be learned by a few steps of gradient updates. While these gradient-based meta-learning models achieve promising performances to some extent, a fundamental problem of them is how to adapt the global knowledge learned from previous tasks for the recommendations of cold-start users more effectively. In this paper, we develop a novel meta-learning recommender called task-adaptive neural process (TaNP). TaNP is a new member of the neural process family, where making recommendations for each user is associated with a corresponding stochastic process. TaNP directly maps the observed interactions of each user to a predictive distribution, sidestepping some training issues in gradient-based meta-learning models. More importantly, to balance the trade-off between model capacity and adaptation reliability, we introduce a novel task-adaptive mechanism. It enables our model to learn the relevance of different tasks and customize the global knowledge to the task-related decoder parameters for estimating user preferences. We validate TaNP on multiple benchmark datasets in different experimental settings. Empirical results demonstrate that TaNP yields consistent improvements over several state-of-the-art meta-learning recommenders.
MAMO: Memory-Augmented Meta-Optimization for Cold-start Recommendation
Authors: Manqing Dong, Feng Yuan, Lina Yao 0001, Xiwei Xu 0001, Liming Zhu 0001
Venue: Conference KDD 2020
Year: 2020

A common challenge for most current recommender systems is the cold-start problem. Due to the lack of user-item interactions, the fine-tuned recommender systems are unable to handle situations with new users or new items. Recently, some works introduce the meta-optimization idea into the recommendation scenarios, i.e. predicting the user preference by only a few of past interacted items. The core idea is learning a global sharing initialization parameter for all users and then learning the local parameters for each user separately. However, most meta-learning based recommendation approaches adopt model-agnostic meta-learning for parameter initialization, where the global sharing parameter may lead the model into local optima for some users. In this paper, we design two memory matrices that can store task-specific memories and feature-specific memories. Specifically, the feature-specific memories are used to guide the model with personalized parameter initialization, while the task-specific memories are used to guide the model fast predicting the user preference. And we adopt a meta-optimization approach for optimizing the proposed method. We test the model on two widely used recommendation datasets and consider four cold-start situations. The experimental results show the effectiveness of the proposed methods.
Leveraging joint interactions for credibility analysis in news communities
Authors: Subhabrata Mukherjee, Gerhard Weikum
Venue: Conference CIKM 2015
Year: 2015

Media seems to have become more partisan, often providing a biased coverage of news catering to the interest of specific groups. It is therefore essential to identify credible information content that provides an objective narrative of an event. News communities such as digg, reddit, or newstrust offer recommendations, reviews, quality ratings, and further insights on journalistic works. However, there is a complex interaction between different factors in such online communities: fairness and style of reporting, language clarity and objectivity, topical perspectives (like political viewpoint), expertise and bias of community members, and more. This paper presents a model to systematically analyze the different interactions in a news community between users, news, and sources. We develop a probabilistic graphical model that leverages this joint interaction to identify 1) highly credible news articles, 2) trustworthy news sources, and 3) expert users who perform the role of "citizen journalists" in the community. Our method extends CRF models to incorporate real-valued ratings, as some communities have very fine-grained scales that cannot be easily discretized without losing information. To the best of our knowledge, this paper is the first full-fledged analysis of credibility, trust, and expertise in news communities.
Integrating pattern-and fact-based fake news detection via model preference learning
Authors: Qiang Sheng, Xueyao Zhang, Juan Cao, Lei Zhong
Venue: Conference CIKM 2021
Year: 2021

To defend against fake news, researchers have developed various methods based on texts. These methods can be grouped as 1) pattern-based methods, which focus on shared patterns among fake news posts rather than the claim itself; and 2) fact-based methods, which retrieve from external sources to verify the claim's veracity without considering patterns. The two groups of methods, which have different preferences of textual clues, actually play complementary roles in detecting fake news. However, few works consider their integration. In this paper, we study the problem of integrating pattern- and fact-based models into one framework via modeling their preference differences, i.e., making the pattern- and fact-based models focus on respective preferred parts in a post and mitigate interference from non-preferred parts as possible. To this end, we build a Preference-aware Fake News Detection Framework (Pref-FEND), which learns the respective preferences of pattern- and fact-based models for joint detection. We first design a heterogeneous dynamic graph convolutional network to generate the respective preference maps, and then use these maps to guide the joint learning of pattern- and fact-based models for final prediction. Experiments on two real-world datasets show that Pref-FEND effectively captures model preferences and improves the performance of models based on patterns, facts, or both.
Multi-Faceted Global Item Relation Learning for Session-Based Recommendation
Authors: Qilong Han, Chi Zhang, Rui Chen, Riwei Lai, Hongtao Song, Li Li
Venue: Conference SIGIR 2022
Year: 2022

As an emerging paradigm, session-based recommendation is aimed at recommending the next item based on a set of anonymous sessions. Effectively representing a session that is normally a short interaction sequence renders a major technical challenge. In view of the limitations of pioneering studies that explore collaborative information from other sessions, in this paper we propose a new direction to enhance session representations by learning multi-faceted session-independent global item relations. In particular, we identify three types of advantageous global item relations, including negative relations that have not been studied before, and propose different graph construction methods to capture such relations. We then devise a novel multi-faceted global item relation (MGIR) model to encode different relations using different aggregation layers and generate enhanced session representations by fusing positive and negative relations. Our solution is flexible to accommodate new item relations and can easily integrate existing session representation learning methods to generate better representations from global relation enhanced session information. Extensive experiments on three benchmark datasets demonstrate the superiority of our model over a large number of state-of-the-art methods. Specifically, we show that learning negative relations is critical for session-based recommendation.
Arabicaqa: A comprehensive dataset for Arabic question answering
Authors: Abdelrahman Abdallah, Mahmoud SalahEldin Kasem, Mahmoud Abdalla, Mohamed Mahmoud, Mohamed Elkasaby, Yasser Elbendary, Adam Jatowt
Venue: Conference SIGIR 2024
Year: 2024

In this paper, we address the significant gap in Arabic natural language processing (NLP) resources by introducing ArabicaQA, the first large-scale dataset for machine reading comprehension and open-domain question answering in Arabic. This comprehensive dataset, consisting of 89,095 answerable and 3,701 unanswerable questions created by crowdworkers to look similar to answerable ones, along with additional labels of open-domain questions marks a crucial advancement in Arabic NLP resources. We also present AraDPR, the first dense passage retrieval model trained on the Arabic Wikipedia corpus, specifically designed to tackle the unique challenges of Arabic text retrieval. Furthermore, our study includes extensive benchmarking of large language models (LLMs) for Arabic question answering, critically evaluating their performance in the Arabic language context. In conclusion, ArabicaQA, AraDPR, and the benchmarking of LLMs in Arabic question answering offer significant advancements in the field of Arabic NLP. The dataset and code are publicly accessible for further research https://github.com/DataScienceUIBK/ArabicaQA.
Learning to answer complex questions over knowledge bases with query composition
Authors: Nikita Bhutani, Xinyi Zheng, H. V. Jagadish
Venue: Conference CIKM 2019
Year: 2019

Recent years have seen a surge of knowledge-based question answering (KB-QA) systems which provide crisp answers to user-issued questions by translating them to precise structured queries over a knowledge base (KB). A major challenge in KB-QA is bridging the gap between natural language expressions and the complex schema of the KB. As a result, existing methods focus on simple questions answerable with one main relation path in the KB and struggle with complex questions that require joining multiple relations. We propose a KB-QA system, TextRay, which answers complex questions using a novel decompose-execute-join approach. It constructs complex query patterns using a set of simple queries. It uses a semantic matching model which is able to learn simple queries using implicit supervision from question-answer pairs, thus eliminating the need for complex query patterns. Our proposed system significantly outperforms existing KB-QA systems on complex questions while achieving comparable results on simple questions.
Pretraining multi-modal representations for chinese ner task with cross-modality attention
Authors: Chengcheng Mai, Mengchuan Qiu, Kaiwen Luo, Ziyan Peng, Jian Liu, Chunfeng Yuan, Yihua Huang
Venue: Conference WSDM 2022
Year: 2022

Named Entity Recognition (NER) aims to identify the pre-defined entities from the unstructured text. Compared with English NER, Chinese NER faces more challenges: the ambiguity problem in entity boundary recognition due to unavailable explicit delimiters between Chinese characters, and the out-of-vocabulary (OOV) problem caused by rare Chinese characters. However, two important features specific to the Chinese language are ignored by previous studies: glyphs and phonetics, which contain rich semantic information of Chinese. To overcome these issues by exploiting the linguistic potential of Chinese as a logographic language, we present MPM-CNER (short for Multi-modal Pretraining Model for Chinese NER), a model for learning multi-modal representations of Chinese semantics, glyphs, and phonetics, via four pretraining tasks: Radical Consistency Identification (RCI), Glyph Image Classification (GIC), Phonetic Consistency Identification (PCI), and Phonetic Classification Modeling (PCM). Meanwhile, a novel cross-modality attention mechanism is proposed to fuse these multimodal features for further improvement. The experimental results show that our method outperforms the state-of-the-art baseline methods on four benchmark datasets, and the ablation study also verifies the effectiveness of the pre-trained multi-modal representations.
Quatre: Relation-aware quaternions for knowledge graph embeddings
Authors: Dai Quoc Nguyen, Thanh Vu, Tu Dinh Nguyen, Dinh Q. Phung
Venue: Conference WWW 2022
Year: 2022

We propose a simple yet effective embedding model to learn quaternion embeddings for entities and relations in knowledge graphs. Our model aims to enhance correlations between head and tail entities given a relation within the Quaternion space with Hamilton product. The model achieves this goal by further associating each relation with two relation-aware rotations, which are used to rotate quaternion embeddings of the head and tail entities, respectively. Experimental results show that our proposed model produces state-of-the-art performances on well-known benchmark datasets for knowledge graph completion. Our code is available at: \url{https://github.com/daiquocnguyen/QuatRE}.
A systematic comparison of methods for finding good premises for claims
Authors: Lorik Dumani, Ralf Schenkel
Venue: Conference SIGIR 2019
Year: 2019

Research on computational argumentation has recently become very popular. An argument consists of a claim that is supported or attacked by at least one premise. Its intention is the persuasion of others. An important problem in this field is retrieving good premises for a designated claim from a corpus of arguments. Given a claim, oftentimes existing approaches' first step is finding textually similar claims. In this paper we compare 196 methods systematically for determining similar claims by textual similarity, using a large corpus of (claim, premise) pairs crawled from debate portals. We also evaluate how well textual similarity of claims can predict relevance of the associated premises.
‘Quality-aware ranking of arguments’
Authors: Lorik Dumani, Ralf Schenkel
Venue: Conference CIKM 2020
Year: 2020

Argument search engines identify, extract, and rank the most important arguments for and against a given controversial topic. A number of such systems have recently been developed, usually focusing on classic information retrieval ranking methods that are based on frequency information. An important aspect that has been ignored so far by search engines is the quality of arguments. We present a quality-aware ranking framework for arguments already extracted from texts and represented as argument graphs, considering multiple established quality measures. An extensive evaluation with a standard benchmark collection demonstrates that taking quality into account significantly helps to improve retrieval quality for argument search. We also publish a dataset in which arguments with respect to topics were tediously annotated by humans with three widely accepted argument quality dimensions.
Extreme multi-label learning for semantic matching in product search
Authors: Wei-Cheng Chang, Daniel L. Jiang, Hsiang-Fu Yu, Choon-Hui Teo, Jiong Zhang, Kai Zhong, Kedarnath Kolluri, Qie Hu, Nikhil Shandilya, Vyacheslav Ievgrafov, Japinder Singh, Inderjit S. Dhillon
Venue: Conference KDD 2021
Year: 2021

We consider the problem of semantic matching in product search: given a customer query, retrieve all semantically related products from a huge catalog of size 100 million, or more. Because of large catalog spaces and real-time latency constraints, semantic matching algorithms not only desire high recall but also need to have low latency. Conventional lexical matching approaches (e.g., Okapi-BM25) exploit inverted indices to achieve fast inference time, but fail to capture behavioral signals between queries and products. In contrast, embedding-based models learn semantic representations from customer behavior data, but the performance is often limited by shallow neural encoders due to latency constraints. Semantic product search can be viewed as an eXtreme Multi-label Classification (XMC) problem, where customer queries are input instances and products are output labels. In this paper, we aim to improve semantic product search by using tree-based XMC models where inference time complexity is logarithmic in the number of products. We consider hierarchical linear models with n-gram features for fast real-time inference. Quantitatively, our method maintains a low latency of 1.25 milliseconds per query and achieves a 65% improvement of Recall@100 (60.9% v.s. 36.8%) over a competing embedding-based DSSM model. Our model is robust to weight pruning with varying thresholds, which can flexibly meet different system requirements for online deployments. Qualitatively, our method can retrieve products that are complementary to existing product search system and add diversity to the match set.
Ngame: Negative mining-aware mini-batching for extreme classification
Authors: Kunal Dahiya, Nilesh Gupta, Deepak Saini, Akshay Soni, Yajun Wang, Kushal Dave, Jian Jiao, Gururaj K, Prasenjit Dey, Amit Singh, Deepesh Hada, Vidit Jain, Bhawna Paliwal, Anshul Mittal, Sonu Mehta, Ramachandran Ramjee, Sumeet Agarwal, Purushottam Kar, Manik Varma
Venue: Conference WSDM 2023
Year: 2023

Extreme Classification (XC) seeks to tag data points with the most relevant subset of labels from an extremely large label set. Performing deep XC with dense, learnt representations for data points and labels has attracted much attention due to its superiority over earlier XC methods that used sparse, hand-crafted features. Negative mining techniques have emerged as a critical component of all deep XC methods, allowing them to scale to millions of labels. However, despite recent advances, training deep XC models with large encoder architectures such as transformers remains challenging. This paper notices that memory overheads of popular negative mining techniques often force mini-batch sizes to remain small and slow training down. In response, this paper introduces NGAME, a light-weight mini-batch creation technique that offers provably accurate in-batch negative samples. This allows training with larger mini-batches offering significantly faster convergence and higher accuracies than existing negative sampling techniques. NGAME was found to be up to 16% more accurate than state-of-the-art methods on a wide array of benchmark datasets for extreme classification, as well as 3% more accurate at retrieving search engine queries in response to a user webpage visit to show personalized ads. In live A/B tests on a popular search engine, NGAME yielded up to 23% gains in click-through-rates. Code for NGAME is available at https://github.com/Extreme-classification/ngame
DECAF: Deep Extreme Classification with Label Features
Authors: Anshul Mittal, Kunal Dahiya, Sheshansh Agrawal, Deepak Saini, Sumeet Agarwal, Purushottam Kar, Manik Varma
Venue: Conference WSDM 2021
Year: 2021

Extreme multi-label classification (XML) involves tagging a data point with its most relevant subset of labels from an extremely large label set, with several applications such as product-to-product recommendation with millions of products. Although leading XML algorithms scale to millions of labels, they largely ignore label meta-data such as textual descriptions of the labels. On the other hand, classical techniques that can utilize label metadata via representation learning using deep networks struggle in extreme settings. This paper develops the DECAF algorithm that addresses these challenges by learning models enriched by label metadata that jointly learn model parameters and feature representations using deep networks and offer accurate classification at the scale of millions of labels. DECAF makes specific contributions to model architecture design, initialization, and training, enabling it to offer up to 2-6% more accurate prediction than leading extreme classifiers on publicly available benchmark product-to-product recommendation datasets, such as LF-AmazonTitles-1.3M. At the same time, DECAF was found to be up to 22x faster at inference than leading deep extreme classifiers, which makes it suitable for real-time applications that require predictions within a few milliseconds. The code for DECAF is available at the following URL https://github.com/Extreme-classification/DECAF.