OpenRaiser/Intern-Atlas huggingface dataset:
Multimodal Machine Learning for Drug Knowledge Discovery
Authors: Guy Shtar
Venue: WSDM
Year: 2021

Multimodal machine learning deals with building models that can process information from multiple modalities (i.e., ways of doing or experiencing something). Experiments involving humans are used to guarantee drug safety in the complex task of drug development. Drug-related data is readily available and comes in various modalities. The proposed study aims to develop novel methods for multimodal machine learning that can be used to process the diverse multimodal data used in drug development and other challenging tasks that could benefit from the use of multimodal data. We present a series of drug-related tasks which are used to both evaluate the models proposed in this ongoing study and discover new drug knowledge. This research will make far-reaching contributions to the field of machine learning, as well as practical contributions in the medical domain.
Triangular Bidword Generation for Sponsored Search Auction
Authors: Zhenqiao Song, Jiaze Chen, Hao Zhou, Lei Li
Venue: WSDM
Year: 2021

Sponsored search auction is a crucial component of modern search engines. It requires a set of candidate bidwords that advertisers can place bids on. Existing methods generate bidwords from search queries or advertisement content. However, they suffer from the data noise in (query, bidword) and (advertisement, bidword) pairs. In this paper, we propose a triangular bidword generation model (TRIDENT), which takes the high-quality data of paired (query, advertisement) as a supervision signal to indirectly guide the bidword generation process. Our proposed model is simple yet effective: by using bidword as the bridge between search query and advertisement, the generation of search query, advertisement and bidword can be jointly learned in the triangular training framework. This alleviates the problem that the training data of bidword may be noisy. Experimental results, including automatic and human evaluations, show that our proposed TRIDENT can generate relevant and diverse bidwords for both search queries and advertisements. Our evaluation on online real data validates the effectiveness of the TRIDENT's generated bidwords for product search.
Beyond Relevance: Trustworthy Answer Selection via Consensus Verification
Authors: Lixin Su, Ruqing Zhang, Jiafeng Guo, Yixing Fan, Jiangui Chen, Yanyan Lan, Xueqi Cheng
Venue: WSDM
Year: 2021

Community Question Answering (CQA) sites such as Yahoo! Answers and Baidu Knows have emerged as rich knowledge resources for information seekers. However, answers posted to CQA sites often vary a lot in their qualities. User votes from the community may partially reflect the overall quality of the answer, but they are often missing. Hence, automatic selection of "good'' answers becomes a practical research problem that will help us manage the quality of accumulated knowledge. Without loss of generality, a good answer should deliver not only relevant but also trustworthy information that can help resolve the information needs of the posted question, but the latter has received less investigation in the past. In this paper, we propose a novel matching-verification framework for automatic answer selection. The matching component assesses the relevance of a candidate answer to a given question as conventional QA methods. The major enhancement is the verification component, which aims to leverage the wisdom of crowds, e.g., some big information repository, for trustworthiness measurement. Given a question, we take the top retrieved results from the information repository as the supporting evidences to distill the consensus representation. A major challenge is that there is no guarantee that one can always obtain reliable consensus from the wisdom of crowds for a question due to the noisy nature and the limitation of the existing search technology.Therefore, we decompose the trustworthiness measurement into two parts, i.e., a verification score which measures the consistency between a candidate answer and the consensus representation, and a confidence score which measures the reliability of the consensus itself. Empirical studies on three real-world CQA data collections, i.e. YahooQA, QuoraQA and AmazonQA, show that our approach can significantly outperform the state-of-the-art methods on the answer selection task.
Sparse-Interest Network for Sequential Recommendation
Authors: Qiaoyu Tan, Jianwei Zhang, Jiangchao Yao, Ninghao Liu, Jingren Zhou, Hongxia Yang, Xia Hu
Venue: WSDM
Year: 2021

Recent methods in sequential recommendation focus on learning an overall embedding vector from a user's behavior sequence for the next-item recommendation. However, from empirical analysis, we discovered that a user's behavior sequence often contains multiple conceptually distinct items, while a unified embedding vector is primarily affected by one's most recent frequent actions. Thus, it may fail to infer the next preferred item if conceptually similar items are not dominant in recent interactions. To this end, an alternative solution is to represent each user with multiple embedding vectors encoding different aspects of the user's intentions. Nevertheless, recent work on multi-interest embedding usually considers a small number of concepts discovered via clustering, which may not be comparable to the large pool of item categories in real systems. It is a non-trivial task to effectively model a large number of diverse conceptual prototypes, as items are often not conceptually well clustered in fine granularity. Besides, an individual usually interacts with only a sparse set of concepts. In light of this, we propose a novel Sparse Interest NEtwork (SINE) for sequential recommendation. Our sparse-interest module can adaptively infer a sparse set of concepts for each user from the large concept pool and output multiple embeddings accordingly. Given multiple interest embeddings, we develop an interest aggregation module to actively predict the user's current intention and then use it to explicitly model multiple interests for next-item prediction. Empirical results on several public benchmark datasets and one large-scale industrial dataset demonstrate that SINE can achieve substantial improvement over state-of-the-art methods.
Adversarial Immunization for Certifiable Robustness on Graphs
Authors: Shuchang Tao, Huawei Shen, Qi Cao, Liang Hou, Xueqi Cheng
Venue: WSDM
Year: 2021

Despite achieving strong performance in semi-supervised node classification task, graph neural networks (GNNs) are vulnerable to adversarial attacks, similar to other deep learning models. Existing researches focus on developing either robust GNN models or attack detection methods against adversarial attacks on graphs. However, little research attention is paid to the potential and practice of immunization to adversarial attacks on graphs. In this paper, we propose and formulate the graph adversarial immunization problem, i.e., vaccinating an affordable fraction of node pairs, connected or unconnected, to improve the certifiable robustness of graph against any admissible adversarial attack. We further propose an effective algorithm, called AdvImmune, which optimizes with meta-gradient in a discrete way to circumvent the computationally expensive combinatorial optimization when solving the adversarial immunization problem. Experiments are conducted on two citation networks and one social network. Experimental results demonstrate that the proposed AdvImmune method remarkably improves the ratio of robust nodes by 12%, 42%, 65%, with an affordable immune budget of only 5% edges.
I2PS: An Online Item Intelligent Publishing System in Consumer to Consumer (C2C) Transaction Platform
Authors: Lei Tian, Nanyang Wang, Pan Pan, Bin Wang, Yinghui Xu
Venue: WSDM
Year: 2021

We present an online Item Intelligent Publishing System used in large scale Consumer to Consumer (C2C) transaction platform, named as I2PS, it is designed for personal seller to publish their items in an automatic way with the uploaded images. Our I2PS is deployed in Xianyu mobile App, the largest second-hand item shopping platform in China. The proposed system not only can guide seller how to photograph the publishing items with more details based on category recognition module, but also can intelligently tell the seller exactly what this product is and which attributes it has based on various recognition methods. The seller does not need to input extra product information, so that the item's publish process in Xianyu can be performed without any difficulty. In this paper, we introduce several techniques we used to develop the I2PS for product understanding, including product's category recognition, Standard Product Unit (SPU) recognition, multi-label attribute recognition and their corresponding pre-processing technologies. Our system deployed in Xianyu can help tens of millions personal sellers to publish their items, and improves publishing success rate by more than 15% and reduces publishing duration by more than 20%. The demo video is available at https://youtu.be/3NRx2hECIHc.
Bilateral Variational Autoencoder for Collaborative Filtering
Authors: Quoc-Tuan Truong, Aghiles Salah, Hady W. Lauw
Venue: WSDM
Year: 2021

Preference data is a form of dyadic data, with measurements associated with pairs of elements arising from two discrete sets of objects. These are users and items, as well as their interactions, e.g., ratings. We are interested in learning representations for both sets of objects, i.e., users and items, to predict unknown pairwise interactions. Motivated by the recent successes of deep latent variable models, we propose Bilateral Variational Autoencoder (BiVAE), which arises from a combination of a generative model of dyadic data with two inference models, user- and item-based, parameterized by neural networks. Interestingly, our model can take the form of a Bayesian variational autoencoder either on the user or item side. As opposed to the vanilla VAE model, BiVAE is "bilateral'', in that users and items are treated similarly, making it more apt for two-way or dyadic data. While theoretically sound, we formally show that, similarly to VAE, our model might suffer from an over-regularized latent space. This issue, known as posterior collapse in the VAE literature, may appear due to assuming an over-simplified prior (isotropic Gaussian) over the latent space. Hence, we further propose a mitigation of this issue by introducing constrained adaptive prior (CAP) for learning user- and item-dependent prior distributions. Empirical results on several real-world datasets show that the proposed model outperforms conventional VAE and other comparative collaborative filtering models in terms of item recommendation. Moreover, the proposed CAP further boosts the performance of BiVAE. An implementation of BiVAE is available on Cornac recommender library.
Question Rewriting for Conversational Question Answering
Authors: Svitlana Vakulenko, Shayne Longpre, Zhucheng Tu, Raviteja Anantha
Venue: WSDM
Year: 2021

Conversational question answering (QA) requires the ability to correctly interpret a question in the context of previous conversation turns. We address the conversational QA task by decomposing it into question rewriting and question answering subtasks. The question rewriting (QR) subtask is specifically designed to reformulate ambiguous questions, which depend on the conversational context, into unambiguous questions that can be correctly interpreted outside of the conversational context. We introduce a conversational QA architecture that sets the new state of the art on the TREC CAsT 2019 passage retrieval dataset. Moreover, we show that the same QR model improves QA performance on the QuAC dataset with respect to answer span extraction, which is the next step in QA after passage retrieval. Our evaluation results indicate that the QR model we proposed achieves near human-level performance on both datasets and the gap in performance on the end-to-end conversational QA task is attributed mostly to the errors in QA.
Non-Clicks Mean Irrelevant? Propensity Ratio Scoring As a Correction
Authors: Nan Wang, Zhen Qin, Xuanhui Wang, Hongning Wang
Venue: WSDM
Year: 2021

Recent advances in unbiased learning to rank (LTR) count on Inverse Propensity Scoring (IPS) to eliminate bias in implicit feedback. Though theoretically sound in correcting the bias introduced by treating clicked documents as relevant, IPS ignores the bias caused by (implicitly) treating non-clicked ones as irrelevant. In this work, we first rigorously prove that such use of click data leads to unnecessary pairwise comparisons between relevant documents, which prevent unbiased ranker optimization. Based on the proof, we derive a simple yet well justified new weighting scheme, called Propensity Ratio Scoring (PRS), which provides treatments on both clicks and non-clicks. Besides correcting the bias in clicks, PRS avoids relevant-relevant document comparisons in LTR training and enjoys a lower variability. Our extensive empirical evaluations confirm that PRS ensures a more effective use of click data and improved performance in both synthetic data from a set of LTR benchmarks, as well as in the real-world large-scale data from GMail search.
Towards Dynamic User Intention in Sequential Recommendation
Authors: Chenyang Wang
Venue: WSDM
Year: 2021

User intention is an important factor to be considered for recommender systems. Different from inherent user preference addressed in traditional recommendation algorithms, which is generally static and consistent, user intention always changes dynamically in different contexts. Recent studies (represented by sequential recommendation) begin to focus on predicting what users want beyond what users like, which can better capture dynamic user intention and have attracted a surge of interest. However, user intention modeling is non-trivial because it is generally influenced by various factors, such as repeat consumption behavior, item relation, temporal dynamics, etc. To better capture dynamic user intention in sequential recommendation, we plan to investigate the influential factors and construct corresponding models to improve the performance. We also want to develop an adaptive way to model temporal evolutions of the effects caused by different factors. Based on the above investigations, we further plan to integrate these factors to deal with extremely long history sequences, where long-term user preference and short-term user demand should be carefully balanced.
Denoising Implicit Feedback for Recommendation
Authors: Wenjie Wang, Fuli Feng, Xiangnan He, Liqiang Nie, Tat-Seng Chua
Venue: WSDM
Year: 2021

The ubiquity of implicit feedback makes them the default choice to build online recommender systems. While the large volume of implicit feedback alleviates the data sparsity issue, the downside is that they are not as clean in reflecting the actual satisfaction of users. For example, in E-commerce, a large portion of clicks do not translate to purchases, and many purchases end up with negative reviews. As such, it is of critical importance to account for the inevitable noises in implicit feedback for recommender training. However, little work on recommendation has taken the noisy nature of implicit feedback into consideration. In this work, we explore the central theme of denoising implicit feedback for recommender training. We find serious negative impacts of noisy implicit feedback, i.e., fitting the noisy data hinders the recommender from learning the actual user preference. Our target is to identify and prune the noisy interactions, so as to improve the efficacy of recommender training. By observing the process of normal recommender training, we find that noisy feedback typically has large loss values in the early stages. Inspired by this observation, we propose a new training strategy named Adaptive Denoising Training (ADT), which adaptively prunes noisy interactions during training. Specifically, we devise two paradigms for adaptive loss formulation: Truncated Loss that discards the large-loss samples with a dynamic threshold in each iteration; and Reweighted Loss that adaptively lowers the weights of large-loss samples. We instantiate the two paradigms on the widely used binary cross-entropy loss and test the proposed ADT strategies on three representative recommenders. Extensive experiments on three benchmarks demonstrate that ADT significantly improves the quality of recommendation over normal training.
CONQ: CONtinuous Quantile Treatment Effects for Large-Scale Online Controlled Experiments
Authors: Weinan Wang, Xi Zhang
Venue: WSDM
Year: 2021

In many industry settings, online controlled experimentation (A/B test) has been broadly adopted as the gold standard to measure product or feature impacts. Most research has primarily focused on user engagement type metrics, specifically measuring treatment effects at mean (average treatment effects, ATE), and only a few have been focusing on performance metrics (e.g. latency), where treatment effects are measured at quantiles. Measuring quantile treatment effects (QTE) is challenging due to the myriad difficulties such as dependency introduced by clustered samples, scalability issues, density bandwidth choices, etc. In addition, previous literature has mainly focused on QTE at some pre-defined locations, such as P50 or P90, which doesn't always convey the full picture. In this paper, we propose a novel scalable non-parametric solution, which can provide a continuous range of QTE with point-wise confidence intervals while circumventing the density estimation altogether. Numerical results show high consistency with traditional methods utilizing asymptotic normality. An end-to-end pipeline has been implemented at Snap Inc., providing daily insights on key performance metrics at a distributional level.
Modeling Context Pair Interaction for Pairwise Tasks on Graphs
Authors: Zhen Wang, Bo Zong, Huan Sun
Venue: WSDM
Year: 2021

Predicting pairwise relationships between nodes in graphs is a fundamental task in data mining with many real-world applications, such as link prediction on social networks, relation prediction on knowledge graphs, etc. A dominating methodology is to first use advanced graph representation methods to learn generic node representations and then build a pairwise prediction classifier with the target nodes' vectors concatenated as input. However, such methods suffer from low interpretability, as it is difficult to explain why certain relationships are predicted only based on their prediction scores. In this paper, we propose to model the pairwise interactions between neighboring nodes (i.e., contexts) of target pairs. The new formulation enables us to build more appropriate representations for node pairs and gain better model interpretability (by highlighting meaningful interactions). To this end, we introduce a unified framework with two general perspectives, node-centric and pair-centric, about how to model context pair interactions. We also propose a novel pair-centric context interaction model and a new pre-trained embedding, which represents the pair semantics and shows many attractive properties. We test our models on two common pairwise prediction tasks: link prediction task and relation prediction task, and compare them with graph feature-based, embedding-based, and Graph Neural Network (GNN)-based baselines. Our experimental results show the superior performance of the pre-trained pair embeddings and that the pair-centric interaction model outperforms all baselines by a large margin.
Modeling Inter-station Relationships with Attentive Temporal Graph Convolutional Network for Air Quality Prediction
Authors: Chunyang Wang, Yanmin Zhu, Tianzi Zang, Haobing Liu, Jiadi Yu
Venue: WSDM
Year: 2021

Air pollution is an important environmental issue of increasing concern, which impacts human health. Accurate air quality prediction is crucial for avoiding people suffering from serious air pollution. Most of the prior works focus on capturing the temporal trend of air quality for each monitoring station. Recent deep learning based methods also model spatial dependencies among neighboring stations. However, we observe that besides geospatially adjacent stations, the stations which share similar functionalities or consistent temporal patterns could also have strong dependencies. In this paper, we propose an Attentive Temporal Graph Convolutional Network (ATGCN) to model diverse inter-station relationships for air quality prediction of citywide stations. Specifically, we first encode three types of relationships among stations including spatial adjacency, functional similarity, and temporal pattern similarity into graphs. Then we design parallel encoding modules, which respectively incorporate attentive graph convolution operations into the Gated Recurrent Units (GRUs) to iteratively aggregate features from related stations with different graphs. Furthermore, augmented with an attention-based fusion unit, decoding modules with a similar structure to the encoding modules are designed to generate multi-step predictions for all stations. The experiments on two real-world datasets demonstrate the superior performance of our model beyond state-of-the-art methods.
Unbiased Learning to Rank in Feeds Recommendation
Authors: Xinwei Wu, Hechang Chen, Jiashu Zhao, Li He, Dawei Yin, Yi Chang
Venue: WSDM
Year: 2021

In feeds recommendation, users are able to constantly browse items generated by never-ending feeds using mobile phones. The implicit feedback from users is an important resource for learning to rank, however, building ranking functions from such observed data is recognized to be biased. The presentation of the items will influence the user's judgements and therefore introduces biases. Most previous works in the unbiased learning to rank literature focus on position bias (i.e., an item ranked higher has more chances of being examined and interacted with). By analyzing user behaviors in product feeds recommendation, in this paper, we identify and introduce context bias, which refers to the probability that a user interacting with an item is biased by its surroundings, to unbiased learning to rank. We propose an Unbiased Learning to Rank with Combinational Propensity (ULTR-CP) framework to remove the inherent biases jointly caused by multiple factors. Under this framework, a context-aware position bias model is instantiated to estimate the unified bias considering both position and context biases. In addition to evaluating propensity score estimation approaches by the ranking metrics, we also discuss the evaluation of the propensities directly by checking their balancing properties. Extensive experiments performed on a real e-commerce data set collected from JD.com verify the effectiveness of context bias and illustrate the superiority of ULTR-CP against the state-of-the-art methods.