OpenRaiser/Intern-Atlas huggingface dataset:
Leveraging Adversarial Training in Self-Learning for Cross-Lingual Text Classification
Authors: Xin Dong 0010, Yaxin Zhu, Yupeng Zhang, Zuohui Fu, Dongkuan Xu, Sen Yang 0002, Gerard de Melo
Venue: SIGIR
Year: 2020

In cross-lingual text classification, one seeks to exploit labeled data from one language to train a text classification model that can then be applied to a completely different language. Recent multilingual representation models have made it much easier to achieve this. Still, there may still be subtle differences between languages that are neglected when doing so. To address this, we present a semi- supervised adversarial training process that minimizes the maximal loss for label-preserving input perturbations. The resulting model then serves as a teacher to induce labels for unlabeled target lan- guage samples that can be used during further adversarial training, allowing us to gradually adapt our model to the target language. Compared with a number of strong baselines, we observe signifi- cant gains in effectiveness on document and intent classification for a diverse set of languages.
ATBRG: Adaptive Target-Behavior Relational Graph Network for Effective Recommendation
Authors: Yufei Feng 0001, Binbin Hu, Fuyu Lv, Qingwen Liu 0002, Zhiqiang Zhang 0012, Wenwu Ou
Venue: SIGIR
Year: 2020

Recommender system (RS) devotes to predicting user preference to a given item and has been widely deployed in most web-scale applications. Recently, knowledge graph (KG) attracts much attention in RS due to its abundant connective information. Existing methods either explore independent meta-paths for user-item pairs over KG, or employ graph neural network (GNN) on whole KG to produce representations for users and items separately. Despite effectiveness, the former type of methods fails to fully capture structural information implied in KG, while the latter ignores the mutual effect between target user and item during the embedding propagation. In this work, we propose a new framework named Adaptive Target-Behavior Relational Graph network (ATBRG for short) to effectively capture structural relations of target user-item pairs over KG. Specifically, to associate the given target item with user behaviors over KG, we propose the graph connect and graph prune techniques to construct adaptive target-behavior relational graph. To fully distill structural information from the sub-graph connected by rich relations in an end-to-end fashion, we elaborate on the model design of ATBRG, equipped with relation-aware extractor layer and representation activation layer. We perform extensive experiments on both industrial and benchmark datasets. Empirical results show that ATBRG consistently and significantly outperforms state-of-the-art methods. Moreover, ATBRG has also achieved a performance improvement of 5.1% on CTR metric after successful deployment in one popular recommendation scenario of Taobao APP.
FinIR 2020: The First Workshop on Information Retrieval in Finance
Authors: Fuli Feng, Cheng Luo 0001, Xiangnan He 0001, Yiqun Liu 0001, Tat-Seng Chua
Venue: SIGIR
Year: 2020

This half-day workshop explores challenges and potential research directions about Information Retrieval (IR) in finance. The focus will be on stimulating discussions around the accessing, searching, filtering, and analyzing financial documents in banking, insurance, and investment, such as the financial statements, analyst reports, filling forms, and news articles. We welcome theoretical, experimental, and methodological studies that aim to advance techniques of managing and understanding financial documents, as well as emphasize the applicability in practical applications. The workshop aims to bring together a diverse set of researchers and practitioners interested in investigating relevant topics. Besides, to facilitate developing and testing some relevant techniques, we hold a data challenge on quantifying analyst reports and news articles for the prediction of commodity prices.
Predicting Perceptual Speed from Search Behaviour
Authors: Olivia Foulds, Alessandro Suglia, Leif Azzopardi, Martin Halvey
Venue: SIGIR
Year: 2020

Perceptual Speed (PS) is a cognitive ability that is known to affect multiple factors in Information Retrieval (IR) such as a user's search performance and subjective experience. However PS tests are difficult to administer which limits the design of user-adaptive systems that can automatically infer PS to appropriately accommodate low PS users. Consequently, this paper evaluated whether PS can be automatically classified from search behaviour using several machine learning models trained on features extracted from TREC Common Core search task logs. Our results are encouraging: given a user's interactions from one query, a Decision Tree was able to predict a user's PS as low or high with 86% accuracy. Additionally, we identified different behavioural components for specific PS tests, implying that each PS test measures different aspects of a person's cognitive ability. These findings motivate further work for how best to design search systems that can adapt to individual differences.
Sampling Bias Due to Near-Duplicates in Learning to Rank
Authors: Maik Fröbe, Janek Bevendorff, Jan Heinrich Reimer, Martin Potthast, Matthias Hagen
Venue: SIGIR
Year: 2020

Learning to rank~(LTR) is the de facto standard for web search, improving upon classical retrieval models by exploiting (in)direct relevance feedback from user judgments, interaction logs, etc. We investigate for the first time the effect of a sampling bias on LTR~models due to the potential presence of near-duplicate web pages in the training data, and how (in)consistent relevance feedback of duplicates influences an LTR~model's decisions. To examine this bias, we construct a series of specialized LTR~datasets based on the ClueWeb09 corpus with varying amounts of near-duplicates. We devise worst-case and average-case train/test splits that are evaluated on popular pointwise, pairwise, and listwise LTR~models. Our experiments demonstrate that duplication causes overfitting and thus less effective models, making a strong case for the benefits of systematic deduplication before training and model evaluation.
BIRDS - Bridging the Gap between Information Science, Information Retrieval and Data Science
Authors: Ingo Frommholz, Haiming Liu 0002, Massimo Melucci
Venue: SIGIR
Year: 2020

The BIRDS workshop aimed to foster the cross-fertilization of Information Science (IS), Information Retrieval (IR) and Data Science (DS). Recognising the commonalities and differences between these communities, the proposed full-day workshop brought together experts and researchers in IS, IR and DS to discuss how they can learn from each other to provide more user-driven data and infor- mation exploration and retrieval solutions. Therefore, the papers aimed to convey ideas on how to utilise, for instance, IS concepts and theories in DS and IR or DS approaches to support users in data and information exploration.
Be Aware of the Hot Zone: A Warning System of Hazard Area Prediction to Intervene Novel Coronavirus COVID-19 Outbreak
Authors: Zhenxin Fu, Yu Wu, Hailei Zhang, Yichuan Hu, Dongyan Zhao 0001, Rui Yan 0001
Venue: SIGIR
Year: 2020

Dating back from late December 2019, the Chinese city of Wuhan has reported an outbreak of atypical pneumonia, now known as lung inflammation caused by novel coronavirus (COVID-19). Cases have spread to other cities in China and more than 180 countries and regions internationally. World Health Organization (WHO) officially declares the coronavirus outbreak a pandemic and the public health emergency is perhaps one of the top concerns in the year of 2020 for governments all over the world. Till today, the coronavirus outbreak is still raging and has no sign of being under control in many countries. In this paper, we aim at drawing lessons from the COVID-19 outbreak process in China and using the experiences to help the interventions against the coronavirus wherever in need. To this end, we have built a system predicting hazard areas on the basis of confirmed infection cases with location information. The purpose is to warn people to avoid of such hot zones and reduce risks of disease transmission through droplets or contacts. We analyze the data from the daily official information release which are publicly accessible. Based on standard classification frameworks with reinforcements incrementally learned day after day, we manage to conduct thorough feature engineering from empirical studies, including geographical, demographic, temporal, statistical, and epidemiological features. Compared with heuristics baselines, our method has achieved promising overall performance in terms of precision, recall, accuracy, F1 score, and AUC. We expect that our efforts could be of help in the battle against the virus, the common opponent of human kind.
Fairness-Aware Explainable Recommendation over Knowledge Graphs
Authors: Zuohui Fu, Yikun Xian, Ruoyuan Gao, Jieyu Zhao 0004, Qiaoying Huang, Yingqiang Ge, Shuyuan Xu, Shijie Geng, Chirag Shah 0001, Yongfeng Zhang 0003, Gerard de Melo
Venue: SIGIR
Year: 2020

There has been growing attention on fairness considerations recently, especially in the context of intelligent decision making systems. For example, explainable recommendation systems may suffer from both explanation bias and performance disparity. We show that inactive users may be more susceptible to receiving unsatisfactory recommendations due to their insufficient training data, and that their recommendations may be biased by the training records of active users due to the nature of collaborative filtering, which leads to unfair treatment by the system. In this paper, we analyze different groups of users according to their level of activity, and find that bias exists in recommendation performance between different groups. Empirically, we find that such performance gap is caused by the disparity of data distribution, specifically the knowledge graph path distribution in this work. We propose a fairness constrained approach via heuristic re-ranking to mitigate this unfairness problem in the context of explainable recommendation over knowledge graphs. We experiment on several real-world datasets with state-of-the-art knowledge graph-based explainable recommendation algorithms. The promising results show that our algorithm is not only able to provide high-quality explainable recommendations, but also reduces the recommendation unfairness in several aspects.
DPLCF: Differentially Private Local Collaborative Filtering
Authors: Chen Gao 0001, Chao Huang, Dongsheng Lin, Depeng Jin, Yong Li 0008
Venue: SIGIR
Year: 2020

Most existing recommender systems leverage users' complete original behavioral logs, which are collected from mobile devices and stored by the service provider and further fed into recommendation models. This may lead to a high risk of privacy leakage since the recommendation service provider may be trustless. Despite many research efforts on privacy-aware recommendation, the problem of building an effective recommender system completely preserving user privacy is still open.
FashionBERT: Text and Image Matching with Adaptive Loss for Cross-modal Retrieval
Authors: Dehong Gao, Linbo Jin, Ben Chen, Minghui Qiu, Peng Li 0056, Yi Wei, Yi Hu, Hao Wang 0005
Venue: SIGIR
Year: 2020

In this paper, we address the text and image matching in cross-modal retrieval of the fashion industry. Different from the matching in the general domain, the fashion matching is required to pay much more attention to the fine-grained information in the fashion images and texts. Pioneer approaches detect the region of interests (i.e., RoIs) from images and use the RoI embeddings as image representations. In general, RoIs tend to represent the "object-level" information in the fashion images, while fashion texts are prone to describe more detailed information, e.g. styles, attributes. RoIs are thus not fine-grained enough for fashion text and image matching. To this end, we propose FashionBERT, which leverages patches as image features. With the pre-trained BERT model as the backbone network, FashionBERT learns high level representations of texts and images. Meanwhile, we propose an adaptive loss to trade off multitask learning in the FashionBERT modeling. Two tasks (i.e., text and image matching and cross-modal retrieval) are incorporated to evaluate FashionBERT. On the public dataset, experiments demonstrate FashionBERT achieves significant improvements in performances than the baseline and state-of-the-art approaches. In practice, FashionBERT is applied in a concrete cross-modal retrieval application. We provide the detailed matching performance and inference efficiency analysis.
Pairwise View Weighted Graph Network for View-based 3D Model Retrieval
Authors: Zan Gao, Yin-Ming Li, Weili Guan, Weizhi Nie, Zhiyong Cheng 0001, Anan Liu
Venue: SIGIR
Year: 2020

View-based 3D model retrieval has become an important task in both computer vision and machine learning domains. Although deep learning methods have achieved excellent performances on view-based 3D model retrieval, the intrinsic correlation and the degree of view discrimination among multiple views in a 3D model have not been effectively exploited. To obtain a more efficient feature descriptor for 3D model retrieval, in this work, we propose the pairwise view weighted graph network (abbreviated PVWGN) for view-based 3D model retrieval where non-local graph layers are embedded into the network architecture to automatically mine the intrinsic relationship among multiple views of a 3D model. Furthermore, the view weighted layer is employed in the PVWGN to adaptively assign the weight to each view according to its aggregation information. In addition, the pairwise discrimination loss function is designed to improve the feature discrimination of the 3D model. Most importantly, these three issues are integrated into a unified framework. Extensive experimental results on the ModelNet40 and ModelNet10 3D model retrieval datasets show that PVWGN can outperform all state-of-the-art methods on the 3D model retrieval task with mAPs of 93.2% and 96.2%, respectively.
Detecting User Community in Sparse Domain via Cross-Graph Pairwise Learning
Authors: Zheng Gao 0001, Hongsong Li, Zhuoren Jiang, Xiaozhong Liu 0001
Venue: SIGIR
Year: 2020

Cyberspace hosts abundant interactions between users and different kinds of objects, and their relations are often encapsulated as bipartite graphs. Detecting user community in such heterogeneous graphs is an essential task to uncover user information needs and to further enhance recommendation performance. While several main cyber domains carrying high-quality graphs, unfortunately, most others can be quite sparse. However, as users may appear in multiple domains (graphs), their high-quality activities in the main domains can supply community detection in the sparse ones, e.g., user behaviors on Google can help thousands of applications to locate his/her local community when s/he uses Google ID to login those applications. In this paper, our model, Pairwise Cross-graph Community Detection (PCCD), is proposed to cope with the sparse graph problem by involving external graph knowledge to learn user pairwise community closeness instead of detecting direct communities. Particularly in our model, to avoid taking excessive propagated information, a two-level filtering module is utilized to select the most informative connections through both community and node level filters. Subsequently, a Community Recurrent Unit (CRU) is designed to estimate pairwise user community closeness. Extensive experiments on two real-world graph datasets validate our model against several strong alternatives. Supplementary experiments also validate its robustness on graphs with varied sparsity scales.
MGNN: A Multimodal Graph Neural Network for Predicting the Survival of Cancer Patients
Authors: Jianliang Gao, Tengfei Lyu, Fan Xiong, Jianxin Wang 0001, Weimao Ke, Zhao Li 0007
Venue: SIGIR
Year: 2020

Predicting the survival of cancer patients holds significant meaning for public health, and has attracted increasing attention in medical information communities. In this study, we propose a novel framework for cancer survival prediction named Multimodal Graph Neural Network (MGNN), which explores the features of real-world multimodual data such as gene expression, copy number alteration and clinical data in a unified framework. In order to explore the inherent relation, we first construct the bipartite graphs between patients and multimodal data. Subsequently, graph neural network is adopted to obtain the embedding of each patient on different bipartite graphs. Finally, a multimodal fusion neural layer is designed to fuse the features from different modal data. The output of our method is the classification of short term survival or long term survival for each patient. Experimental results on one breast cancer dataset demonstrate that MGNN outperforms all baselines. Furthermore, we test the trained model on lung cancer dataset, and the experimental results verify the strong robust by comparing with state-of-the-art methods.
Relevance Transformer: Generating Concise Code Snippets with Relevance Feedback
Authors: Carlos Gemmell, Federico Rossetto, Jeffrey Dalton 0001
Venue: SIGIR
Year: 2020

Tools capable of automatic code generation have the potential to augment programmer's capabilities. While straightforward code retrieval is incorporated into many IDEs, an emerging area is explicit code generation. Code generation is currently approached as a Machine Translation task, with Recurrent Neural Network (RNN) based encoder-decoder architectures trained on code-description pairs. In this work we introduce and study modern Transformer architectures for this task. We further propose a new model called the Relevance Transformer that incorporates external knowledge using pseudo-relevance feedback. The Relevance Transformer biases the decoding process to be similar to existing retrieved code while enforcing diversity. We perform experiments on multiple standard benchmark datasets for code generation including Django, Hearthstone, and CoNaLa. The results show improvements over state-of-the-art methods based on BLEU evaluation. The Relevance Transformer model shows the potential of Transformer-based architectures for code generation and introduces a method of incorporating pseudo-relevance feedback during inference.
Explaining Recommendations in Heterogeneous Networks
Authors: Azin Ghazimatin
Venue: SIGIR
Year: 2020

Users are increasingly relying on personalized recommendations (such as news, songs, products) for their daily information consumption. To deliver personalized content to users, Heterogeneous Information Network (HIN)-based recommender systems integrate various data collected from users into an often complex ranking model. The resulting recommendations might thus be puzzling for the users, leaving them wondering why some particular items are recommended to them or how these items relate to their actions on the platform. Therefore, to gain users' trust, it is crucial to provide them with explanations for their recommendations.
Efficient and Effective Query Auto-Completion
Authors: Simon Gog, Giulio Ermanno Pibiri, Rossano Venturini
Venue: SIGIR
Year: 2020

Query Auto-Completion (QAC) is an ubiquitous feature of modern textual search systems, suggesting possible ways of completing the query being typed by the user. Efficiency is crucial to make the system have a real-time responsiveness when operating in the million-scale search space. Prior work has extensively advocated the use of a trie data structure for fast prefix-search operations in compact space. However, searching by prefix has little discovery power in that only completions that are prefixed by the query are returned. This may impact negatively the effectiveness of the QAC system, with a consequent monetary loss for real applications like Web Search Engines and eCommerce.
Session-based Recommendation with Hierarchical Leaping Networks
Authors: Cheng Guo, Mengfei Zhang, Jinyun Fang, Jiaqi Jin, Mao Pan
Venue: SIGIR
Year: 2020

Session-based recommendation aims to predict the next item that users will interact based solely on anonymous sessions. In real-life scenarios, the user's preferences are usually various, and distinguishing different preferences in the session is important. However previous studies focus mostly on the transition modeling between items, ignoring the mining of various user preferences. In this paper, we propose a Hierarchical Leaping Network (HLN) to explicitly model the users' multiple preferences by grouping items that share some relationships. We first design a Leap Recurrent Unit (LRU) which is capable of skipping preference-unrelated items and accepting knowledge of previously learned preferences. Then we introduce a Preference Manager (PM) to manage those learned preferences and produce an aggregated preference representation each time LRU reruns. The final output of PM which contains multiple preferences of the user is used to make recommendations. Experiments on two benchmark datasets demonstrate the effectiveness of HLN. Furthermore, the visualization of explicitly learned subsequences also confirms our idea.
Multi-Branch Convolutional Network for Context-Aware Recommendation
Authors: Wei Guo 0006, Can Zhang 0007, Huifeng Guo, Ruiming Tang, Xiuqiang He 0001
Venue: SIGIR
Year: 2020

Factorization Machine (FM)-based models can only reveal the relationship between a pair of features. With all feature embeddings fed to a MLP, DNN-based factorization models which combine FM with multi-layer perceptron (MLP) can only reveal the relationship among some features implicitly. Some other DNN-based methods apply CNN to generate feature interactions. However, (1) they model feature interactions at the bit-wise (where only part of an embedding is utilized to generate feature interactions), which can not express the semantics of features comprehensively, (2) they can only model the interactions among the neighboring features. To deal with aforementioned problems, this paper proposes a Multi-Branch Convolutional Network (MBCN) which includes three branches: the standard convolutional layer, the dilated convolutional layer and the bias layer. MBCN is able to explicitly model feature interactions with arbitrary orders at the vector-wise, which fully express context-aware feature semantics. Extensive experiments on three public benchmark datasets are conducted to demonstrate the superiority of MBCN, compared to the state-of-the-art baselines for context-aware top-k recommendation.
Efficient Image Gallery Representations at Scale Through Multi-Task Learning
Authors: Benjamin Gutelman, Pavel Levin
Venue: SIGIR
Year: 2020

Image galleries provide a rich source of diverse information about a product which can be leveraged across many recommendation and retrieval applications. We study the problem of building a universal image gallery encoder through multi-task learning (MTL) approach and demonstrate that it is indeed a practical way to achieve generalizability of learned representations to new downstream tasks. Additionally, we analyze the relative predictive performance of MTL-trained solutions against optimal and substantially more expensive solutions, and find signals that MTL can be a useful mechanism to address sparsity in low-resource binary tasks.