OpenRaiser/Intern-Atlas huggingface dataset:
SCSS-LIE: A Novel Synchronous Collaborative Search System with a Live Interactive Engine
Authors: Junyan Wang, Peng Zhang, Cheng Zhang, Dawei Song
Venue: SIGIR
Year: 2019

Synchronous collaborative search systems (SCSS) refer to systems which support two or more users with similar information need to search together simultaneously. Generally, SCSS provide a social engine to enable users to communicate. However, when the number of users in the social engine is insufficient to collaborate on the search task, the social engine will encounter the cold start problem and can not perform collaborative search well. In this paper, we present a novel Synchronous Collaborative Search System with a Live Interactive Engine (SCSS-LIE). SCSS-LIE proposes to apply a ring topology to add an intelligent auxiliary robot, Infobot, into the social engine to support real-time interaction between users and the search engine to address the cold start problem of the social engine. The reading comprehension model BiDAF (Bi-Directional Attention Flow) is employed in the Infobot in the process of interacting with the search engine to obtain answers to facilitate the acquisition of information. SCSS-LIE can not only allow users with similar information need to be grouped into one chat channel to communicate, but also enable them to conduct real-time interaction with the search engine to improve search efficiency.
Dynamic Content Monitoring and Exploration using Vector Spaces
Authors: Benyou Wang
Venue: SIGIR
Year: 2019

This doctoral research project investigates using Quantum Theory (QT) to represent language, especially in some dynamic scenarios, e.g. when dealing with dynamic corpora or interactive tasks. The author plans to propose a quantum state driven framework for language problems and generalize it in a high-dimensional tensor space. Dynamics will be modeled by the formalism thereof of quantum evolution governing the update of quantum states. The author argues that this proposal will pave the way towards a new paradigm which may provide some novel insights about how to represent the language and its evolution in dynamic scenarios.
Unified Collaborative Filtering over Graph Embeddings
Authors: Pengfei Wang, Hanxiong Chen, Yadong Zhu, Huawei Shen, Yongfeng Zhang
Venue: SIGIR
Year: 2019

Collaborative Filtering (CF) by learning from the wisdom of crowds has become one of the most important approaches to recommender systems research, and various CF models have been designed and applied to different scenarios. However, a challenging task is how to select the most appropriate CF model for a specific recommendation task. In this paper, we propose a Unified Collaborative Filtering framework based on Graph Embeddings (UGrec for short) to solve the problem. Specifically, UGrec models user and item interactions within a graph network, and sequential recommendation path is designed as a basic unit to capture the correlations between users and items. Mathematically, we show that many representative recommendation approaches and their variants can be mapped as a recommendation path in the graph. In addition, by applying a carefully designed attention mechanism on the recommendation paths, UGrec can determine the significance of each sequential recommendation path so as to conduct automatic model selection. Compared with state-of-the-art methods, our method shows significant improvements for recommendation quality. This work also leads to a deeper understanding of the connection between graph embeddings and recommendation algorithms.
Hierarchical Matching Network for Crime Classification
Authors: Pengfei Wang, Yu Fan, Shuzi Niu, Ze Yang, Yongfeng Zhang, Jiafeng Guo
Venue: SIGIR
Year: 2019

Automatic crime classification is a fundamental task in the legal field. Given the fact descriptions, judges first determine the relevant violated laws, and then the articles. As laws and articles are grouped into a tree-shaped hierarchy (i.e., laws as parent labels, articles as children labels), this task can be naturally formalized as a two layers' hierarchical multi-label classification problem. Generally, the label semantics (i.e., definition of articles) and the hierarchical structure are two informative properties for judges to make a correct decision. However, most previous methods usually ignore the label structure and feed all labels into a flat classification framework, or neglect the label semantics and only utilize fact descriptions for crime classification, thus the performance may be limited. In this paper, we formalize crime classification problem into a matching task to address these issues. We name our model as Hierarchical Matching Network (HMN for short). Based on the tree hierarchy, HMN explicitly decomposes the semantics of children labels into the residual and alignment components. The residual components keep the unique characteristics of each individual children label, while the alignment components capture the common semantics among sibling children labels, which are further aggregated as the representation of their parent label. Finally, given a fact description, a co-attention metric is applied to effectively match the relevant laws and articles. Experiments on two real-world judicial datasets demonstrate that our model can significantly outperform the state-of-the-art methods.
Variance Reduction in Gradient Exploration for Online Learning to Rank
Authors: Huazheng Wang, Sonwoo Kim, Eric McCord-Snook, Qingyun Wu, Hongning Wang
Venue: SIGIR
Year: 2019

Online Learning to Rank (OL2R) algorithms learn from implicit user feedback on the fly. The key to such algorithms is an unbiased estimate of gradients, which is often (trivially) achieved by uniformly sampling from the entire parameter space. Unfortunately, this leads to high-variance in gradient estimation, resulting in high regret during model updates, especially when the dimension of the parameter space is large. In this work, we aim at reducing the variance of gradient estimation in OL2R algorithms. We project the selected updating direction (i.e., the winning direction) into a space spanned by the feature vectors from examined documents under the current query (termed the "document space" for short), after an interleaved test. Our key insight is that the result of an interleaved test is solely governed by a user's relevance evaluation over the examined documents. Hence, the true gradient introduced by this test is only reflected in the constructed document space, and components of the proposed gradient which are orthogonal to the document space can be safely removed, for variance reduction purpose. We prove that this projected gradient is still an unbiased estimation of the true gradient, and show that this lower-variance gradient estimation results in significant regret reduction. Our proposed method is compatible with all existing OL2R algorithms which rank documents using a linear model. Extensive experimental comparisons with several state-of-the-art OL2R algorithms have confirmed the effectiveness of our proposed method in reducing the variance of gradient estimation and improving overall ranking performance.
Effective Medical Archives Processing Using Knowledge Graphs
Authors: Xiaoli Wang, Rongzhen Wang, Zhifeng Bao, Jiayin Liang, Wei Lu
Venue: SIGIR
Year: 2019

Medical archives processing is a very important task in a medical information system. It generally consists of three steps: medical archives recognition, feature extraction and text classification. In this paper, we focus on empowering the medical archives processing with knowledge graphs. We first build a semantic-rich medical knowledge graph. Then, we recognize texts from medical archives using several popular optical character recognition (OCR) engines, and extract keywords from texts using a knowledge graph based feature extraction algorithm. Third, we define a semantic measure based on knowledge graph to evaluate the similarity between medical texts, and perform the text classification task. This measure can value semantic relatedness between medical documents, to enhance the text classification. We use medical archives collected from real hospitals for validation. The results show that our algorithms can significantly outperform typical baselines that employs only term statistics.
Encoding Syntactic Dependency and Topical Information for Social Emotion Classification
Authors: Chang Wang, Bang Wang, Wei Xiang, Minghua Xu
Venue: SIGIR
Year: 2019

Social emotion classification is to estimate the distribution of readers' emotion evoked by an article. In this paper, we design a new neural network model by encoding sentence syntactic dependency and document topical information into the document representation. We first use a dependency embedded recursive neural network to learn syntactic features for each sentence, and then use a gated recurrent unit to transform the sentences' vectors into a document vector. We also use a multi-layer perceptron to encode the topical information of a document into a topic vector. Finally, a gate layer is used to compose the document representation from the gated summation of the document vector and the topic vector. Experiment results on two public datasets indicate that our proposed model outperforms the state-of-the-art methods in terms of better average Pearson correlation coefficient and MicroF1 performance.
Document Gated Reader for Open-Domain Question Answering
Authors: Bingning Wang, Ting Yao, Qi Zhang, Jingfang Xu, Zhixing Tian, Kang Liu, Jun Zhao
Venue: SIGIR
Year: 2019

Open-domain question answering focuses on using diverse information resources to answer any types of question. Recent years, with the development of large-scale data set and various deep neural networks models, some recent advances in open domain question answering system first utilize the distantly supervised dataset as the knowledge resource, then apply deep learning based machine comprehension techniques to generate the right answers, which achieves impressive results compared with traditional feature-based pipeline methods. However, these deep learning based methods suffer from the inferior quality of the distantly supervised data, and the answer score is un-normalized among multiple documents. Furthermore, unlike previous open-domain question answering system, they process each document independently which may ignore the valuable information in the context. In this paper, we propose a document gated reader to generate the right answer from multiple documents. We propose a document-level gate operation to determine the question-document relevance and embed it into the answer generation process, and optimize it with the global normalization objective. We also develop a bootstrapping based data generation scheme to obtain high-quality training data. Experimental results on several question answering datasets show the advantage of the proposed methods.
Deep Collaborative Discrete Hashing with Semantic-Invariant Structure
Authors: Zijian Wang, Zheng Zhang, Yadan Luo, Zi Huang
Venue: SIGIR
Year: 2019

Existing deep hashing approaches fail to fully explore semantic correlations and neglect the effect of linguistic context on visual attention learning, leading to inferior performance. This paper proposes a dual-stream learning framework, dubbed Deep Collaborative Discrete Hashing (DCDH), which constructs a discriminative common discrete space by collaboratively incorporating the shared and individual semantics deduced from visual features and semantic labels. Specifically, the context-aware representations are generated by employing the outer product of visual embeddings and semantic encodings. Moreover, we reconstruct the labels and introduce the focal loss to take advantage of frequent and rare concepts. The common binary code space is built on the joint learning of the visual representations attended by language, the semantic-invariant structure construction and the label distribution correction. Extensive experiments demonstrate the superiority of our method.
Task Completion Detection: A Study in the Context of Intelligent Systems
Authors: Ryen W. White, Ahmed Hassan Awadallah, Robert Sim
Venue: SIGIR
Year: 2019

People can record their pending tasks using to-do lists, digital assistants, and other task management software. In doing so, users of these systems face at least two challenges: (1) they must manually mark their tasks as complete, and (2) when systems proactively remind them about their pending tasks, say, via interruptive notifications, they lack information on task completion status. As a result, people may not realize the full benefits of to-do lists (since these lists can contain both completed and pending tasks) and they may be reminded about tasks they have already done (wasting time and causing frustration). In this paper, we present methods to automatically detect task completion. These inferences can be used to deprecate completed tasks and/or suppress notifications for these tasks (or for other purposes, e.g., task prioritization). Using log data from a popular digital assistant, we analyze temporal dynamics in the completion of tasks and train machine-learned models to detect completion with accuracy exceeding 80% using a variety of features (time elapsed since task creation, task content, email, notifications, user history). The findings have implications for the design of intelligent systems to help people manage their tasks.
Measuring Job Search Effectiveness
Authors: Alfan Farizki Wicaksono
Venue: SIGIR
Year: 2019

Users of online job search websites interact with ranked lists of job summaries generated in response to queries, hoping to identify one or more jobs of interest. Hence, the quality of job search rankings becomes a primary factor that affects user satisfaction. In this work, we propose methodologies and measures for evaluating the quality of job search rankings from a user modeling perspective. We start by investigating job seekers' behavior when they are interacting with the generated rankings, leveraging job search interaction logs from Seek.com, a well-known Australasian job search website. The output of this investigation will be an accurate model of job seekers that will be incorporated into an effectiveness metric. Recent proposals for job search ranking models used using two types of metrics to evaluate the quality of the ranking generated by the models: (1) offline metrics, such as NDCG@k (k is set to the number of job summaries shown in the first page), Prec@1, or Mean Reciprocal Rank (MRR); and (2) online metrics, such as click-through rate and job application rate [3, 6].
Document Distance Metric Learning in an Interactive Exploration Process
Authors: Marco Wrzalik
Venue: SIGIR
Year: 2019

Visualization of inter-document similarities is widely used for the exploration of document collections and interactive retrieval. However, similarity relationships between documents are multifaceted and measured distances by a given metric often do not match the perceived similarity of human beings. Furthermore, the user's notion of similarity can drastically change with the exploration objective or task at hand. Therefore, this research proposes to investigate online adjustments to the similarity model using feedback generated during exploration or exploratory search. In this course, rich visualizations and interactions will support users to give valuable feedback. Based on this, metric learning methodologies will be applied to adjust a similarity model in order to improve the exploration experience. At the same time, trained models are considered as valuable outcomes whose benefits for similarity-based tasks such as query-by-example retrieval or classification will be tested.
One-Class Collaborative Filtering with the Queryable Variational Autoencoder
Authors: Ga Wu, Mohamed Reda Bouadjenek, Scott Sanner
Venue: SIGIR
Year: 2019

Variational Autoencoder (VAE) based methods for Collaborative Filtering (CF) demonstrate remarkable performance for one-class (implicit negative) recommendation tasks by extending autoencoders with relaxed but tractable latent distributions. Explicitly modeling a latent distribution over user preferences allows VAEs to learn user and item representations that not only reproduce observed interactions, but also generalize them by leveraging learning from similar users and items. Unfortunately, VAE-CF can exhibit suboptimal learning properties; e.g., VAE-CFs will increase their prediction confidence as they receive more preferences per user, even when those preferences may vary widely and create ambiguity in the user representation. To address this issue, we propose a novel Queryable Variational Autoencoder (Q-VAE) variant of the VAE that explicitly models arbitrary conditional relationships between observations. The proposed model appropriately increases uncertainty (rather than reduces it) in cases where a large number of user preferences may lead to an ambiguous user representation. Our experiments on two benchmark datasets show that the Q-VAE generally performs comparably or outperforms VAE-based recommenders as well as other state-of-the-art approaches and is generally competitive across the user preference density spectrum, where other methods peak for certain preference density levels.
A Neural Influence Diffusion Model for Social Recommendation
Authors: Le Wu, Peijie Sun, Yanjie Fu, Richang Hong, Xiting Wang, Meng Wang
Venue: SIGIR
Year: 2019

Precise user and item embedding learning is the key to building a successful recommender system. Traditionally, Collaborative Filtering (CF) provides a way to learn user and item embeddings from the user-item interaction history. However, the performance is limited due to the sparseness of user behavior data. With the emergence of online social networks, social recommender systems have been proposed to utilize each user's local neighbors' preferences to alleviate the data sparsity for better user embedding modeling. We argue that, for each user of a social platform, her potential embedding is influenced by her trusted users, with these trusted users are influenced by the trusted users' social connections. As social influence recursively propagates and diffuses in the social network, each user's interests change in the recursive process. Nevertheless, the current social recommendation models simply developed static models by leveraging the local neighbors of each user without simulating the recursive diffusion in the global social network, leading to suboptimal recommendation performance. In this paper, we propose a deep influence propagation model to stimulate how users are influenced by the recursive social diffusion process for social recommendation. For each user, the diffusion process starts with an initial embedding that fuses the related features and a free user latent vector that captures the latent behavior preference. The key idea of our proposed model is that we design a layer-wise influence propagation structure to model how users' latent embeddings evolve as the social diffusion process continues. We further show that our proposed model is general and could be applied when the user~(item) attributes or the social network structure is not available. Finally, extensive experimental results on two real-world datasets clearly show the effectiveness of our proposed model, with more than 13% performance improvements over the best baselines for top-10 recommendation on the two datasets.
Deep Chit-Chat: Deep Learning for Chatbots
Authors: Wei Wu, Rui Yan
Venue: SIGIR
Year: 2019

The tutorial is based on our long-term research on open domain conversation, rich hands-on experience on development of Microsoft XiaoIce, and our previous tutorials on EMNLP 2018 and the Web Conference 2019. It starts from a summary of recent achievement made by both academia and industry on chatbots, and then performs a thorough and systematic introduction to state-of-the-art methods for open domain conversation modeling including both retrieval-based methods and generation-based methods. In addition to these, the tutorial also covers some new progress on both groups of methods, such as transition from model design to model learning, transition from knowledge agnostic conversation to knowledge aware conversation, and transition from single-modal conversation to multi-modal conversation. The tutorial is ended by some promising future directions such as how to combine non-task-oriented dialogue systems with task-oriented dialogue systems and how to enhance language learning with chatbots.
Reinforcement Knowledge Graph Reasoning for Explainable Recommendation
Authors: Yikun Xian, Zuohui Fu, S. Muthukrishnan, Gerard de Melo, Yongfeng Zhang
Venue: SIGIR
Year: 2019

Recent advances in personalized recommendation have sparked great interest in the exploitation of rich structured information provided by knowledge graphs. Unlike most existing approaches that only focus on leveraging knowledge graphs for more accurate recommendation, we aim to conduct explicit reasoning with knowledge for decision making so that the recommendations are generated and supported by an interpretable causal inference procedure. To this end, we propose a method called Policy-Guided Path Reasoning (PGPR), which couples recommendation and interpretability by providing actual paths in a knowledge graph. Our contributions include four aspects. We first highlight the significance of incorporating knowledge graphs into recommendation to formally define and interpret the reasoning process. Second, we propose a reinforcement learning (RL) approach featured by an innovative soft reward strategy, user-conditional action pruning and a multi-hop scoring function. Third, we design a policy-guided graph search algorithm to efficiently and effectively sample reasoning paths for recommendation. Finally, we extensively evaluate our method on several large-scale real-world benchmark datasets, obtaining favorable results compared with state-of-the-art methods.
AliISA: Creating an Interactive Search Experience in E-commerce Platforms
Authors: Fei Xiao, Zhen Wang, Haikuan Huang, Jun Huang, Xi Chen, Hongbo Deng, Minghui Qiu, Xiaoli Gong
Venue: SIGIR
Year: 2019

Online shopping has been a habit of more and more people, while most users are unable to craft an informative query, and thus it often takes a long search session to satisfy their purchase intents. We present AliISA - a shopping assistant which offers users some tips to further specify their queries during a search session. With such an interactive search, users tend to find targeted items with fewer page requests, which often means a better user experience. Currently, AliISA assists tens of millions of users per day, earns more usage than existing systems, and consequently brings in a 5% improvement in CVR. In this paper, we present our system, describe the underlying techniques, and discuss our experience in stabilizing reinforcement learning under an E-commerce environment.
Revealing the Role of User Moods in Struggling Search Tasks
Authors: Luyan Xu, Xuan Zhou, Ujwal Gadiraju
Venue: SIGIR
Year: 2019

User-centered approaches have been extensively studied and used in the area of struggling search. Related research has targeted key aspects of users such as user satisfaction or frustration, and search success or failure, using a variety of experimental methods including laboratory user studies, in-situ explicit feedback from searchers and by using crowdsourcing. Such studies are valuable in advancing the understanding of search difficulty from a user's perspective, and yield insights that can directly improve search systems and their evaluation. However, little is known about how user moods influence their interactions with a search system or their perception of struggling. In this work, we show that a user's own mood. can systematically bias the user's perception, and experience while interacting with a search system and trying to satisfy an information need. People who are in activated-(un)pleasant moods tend to issue more queries than people in deactivated or neutral moods. Those in an unpleasant mood perceive a higher level of difficulty. Our insights extend the current understanding of struggling search tasks and have important implications on the design and evaluation of search systems supporting such tasks.