OpenRaiser/Intern-Atlas huggingface dataset:
Proactive Information Retrieval via Screen Surveillance
Authors: Vuong Thanh Tung, Giulio Jacucci, Tuukka Ruotsalo
Venue: SIGIR
Year: 2017

We demonstrate proactive information retrieval via screen surveillance. A user's digital activities are continuously monitored by capturing all content on a user's screen using optical character recognition. This includes all applications and services being exploited and relies on each individual user's computer usage, such as their Web browsing, emails, instant messaging, and word processing. Topic modeling is then applied to detect the user's topical activity context to retrieve information. We demonstrate a system that proactively retrieves information from a user's activity history being observed on the screen when the user is performing unseen activities on a personal computer. We report an evaluation with ten participants that shows high user satisfaction and retrieval effectiveness. Our demonstration and experimental results show that surveillance of a user's screen can be used to build an extremely rich model of a user's digital activities across application boundaries and enable effective proactive information retrieval.
Predictive Network Representation Learning for Link Prediction
Authors: Zhitao Wang, Chengyao Chen, Wenjie Li
Venue: SIGIR
Year: 2017

In this paper, we propose a predictive network representation learning (PNRL) model to solve the structural link prediction problem. The proposed model defines two learning objectives, i.e., observed structure preservation and hidden link prediction. To integrate the two objectives in a unified model, we develop an effective sampling strategy to select certain edges in a given network as assumed hidden links and regard the rest network structure as observed when training the model. By jointly optimizing the two objectives, the model can not only enhance the predictive ability of node representations but also learn additional link prediction knowledge in the representation space. Experiments on four real-world datasets demonstrate the superiority of the proposed model over the other popular and state-of-the-art approaches.
From Footprint to Friendship: Modeling User Followership in Mobile Social Networks from Check-in Data
Authors: Cheng Wang, Jieren Zhou, Bo Yang
Venue: SIGIR
Year: 2017

In this paper we aim at addressing the correlation between two critical factors in mobile social networks (MSNs): the social-relationship networking among users and the spatial mobility pattern of users. Specifically, we investigate the impact of users' spatial distribution on their social relationship formation in MSNs. Based on the geolocation data (check-in records) and social relation data of MSN users, we propose a model, called neighborhood-cardinality-based model (NCBM), to describe this impact by taking into account both the multiple home-points/hotspots property of spatial mobility and the long-tailed social relationship degree distribution of MSN users. We define a fundamental quantity for each user, i.e., the so-called neighborhood cardinality, to measure how many and how often other MSN users visit his nearby area with a given range. The core of NCBM is a principle: The probability that a user, say u, is followed by another user, say v, obeys a power law distribution of the neighborhood cardinality of user u. The proposed formation model is evaluated on two large check-in datasets: Brightkite and Gowalla. Our experimental results indicate that the proposed formation model provides a useful paradigm for capturing the correlation between MSN users' mobility patterns and social relationships.
Reinforcement Learning to Rank with Markov Decision Process
Authors: Zheng Wei, Jun Xu, Yanyan Lan, Jiafeng Guo, Xueqi Cheng
Venue: SIGIR
Year: 2017

One of the central issues in learning to rank for information retrieval is to develop algorithms that construct ranking models by directly optimizing evaluation measures such as normalized discounted cumulative gain~(ND CG). Existing methods usually focus on optimizing a specific evaluation measure calculated at a fixed position, e.g., NDCG calculated at a fixed position K. In information retrieval the evaluation measures, including the widely used NDCG and [email protected], are usually designed to evaluate the document ranking at all of the ranking positions, which provide much richer information than only measuring the document ranking at a single position. Thus, it is interesting to ask if we can devise an algorithm that has the ability of leveraging the measures calculated at all of the ranking postilions, for learning a better ranking model. In this paper, we propose a novel learning to rank model on the basis of Markov decision process (MDP), referred to as MDPRank. In the learning phase of MDPRank, the construction of a document ranking is considered as a sequential decision making, each corresponds to an action of selecting a document for the corresponding position. The policy gradient algorithm of REINFORCE is adopted to train the model parameters. The evaluation measures calculated at every ranking positions are utilized as the immediate rewards to the corresponding actions, which guide the learning algorithm to adjust the model parameters so that the measure is optimized. Experimental results on LETOR benchmark datasets showed that MDPRank can outperform the state-of-the-art baselines.
Improving Search Engines via Large-Scale Physiological Sensing
Authors: Ryen W. White, Ryan Ma
Venue: SIGIR
Year: 2017

Result ranking in commercial web search engines is based on a wide array of signals, from keywords appearing on web pages to behavioral (clickthrough) data aggregated across many users or from the current user only. The recent emergence of wearable devices has enabled the collection of physiological data such as heart rate, skin temperature, and galvanic skin response at a population scale. These data are useful for many public health tasks, but they may also provide novel clues about people's interests and intentions as they engage in online activities. In this paper, we focus on heart rate and show that there are strong relationships between heart rate and various measures of user interest in a search result. We integrate features of heart rate, including heart rate dynamics, as additional attributes in a competitive machine-learned web search ranking algorithm. We show that we can obtain significant relevance improvements from this physiological sensing that vary depending on the search topic.
Teaching the Information Retrieval Process Using a Web-Based Environment and Game Mechanics
Authors: Thomas Wilhelm-Stein, Stefan Kahl, Maximilian Eibl
Venue: SIGIR
Year: 2017

Predicting the performance of individual components of information retrieval systems, in particular the complex interactions between those components, is still challenging. Therefore, professionals are needed for the implementation and configuration of retrieval systems and retrieval components. Our web-based application, called Xtrieval Web Lab, enables newcomers and learners to gain practical knowledge about the information retrieval process. They can arrange a multitude of components of retrieval systems and evaluate them with real world data without utilizing a programming language. Game mechanics guide the learners in their discovery process and motivate them.
ReviewMiner: An Aspect-based Review Analytics System
Authors: Derek Wu, Hongning Wang
Venue: SIGIR
Year: 2017

We develop an aspect-based sentiment analysis system named ReviewMiner. It analyzes opinions expressed about an entity in an online review at the level of topical aspects to discover each individual reviewer's latent opinion on each aspect as well as his/her relative emphasis on different aspects when forming the overall judgment of the entity. The system personalizes the retrieved results according to users' input preferences over the identified aspects, recommends similar items based on the detailed aspect-level opinions, and summarizes aspect-level opinions in textual, temporal and spatial dimensions. The unique multi-modal opinion summarization and visualization mechanisms provide users with rich perspectives to digest information from user-generated opinionated content for making informed decisions.
Adapting Markov Decision Process for Search Result Diversification
Authors: Long Xia, Jun Xu, Yanyan Lan, Jiafeng Guo, Wei Zeng, Xueqi Cheng
Venue: SIGIR
Year: 2017

In this paper we address the issue of learning diverse ranking models for search result diversification. Typical methods treat the problem of constructing a diverse ranking as a process of sequential document selection. At each ranking position, the document that can provide the largest amount of additional information to the users is selected, because the search users usually browse the documents in a top-down manner. Thus, to select an optimal document for a position, it is critical for a diverse ranking model to capture the utility of information the user have perceived from the preceding documents. Existing methods usually calculate the ranking scores (e.g., the marginal relevance) directly based on the query and the selected documents, with heuristic rules or handcrafted features. The utility the user perceived at each of the ranks, however, is not explicitly modeled. In this paper, we present a novel diverse ranking model on the basis of continuous state Markov decision process (MDP) in which the user perceived utility is modeled as a part of the MDP state. Our model, referred to as MDP-DIV, sequentially takes the actions of selecting one document according to current state, and then updates the state for the chosen of the next action. The transition of the states are modeled in a recurrent manner and the model parameters are learned with policy gradient. Experimental results based on the TREC benchmarks showed that MDP-DIV can significantly outperform the state-of-the-art baselines.
De-duping URLs with Sequence-to-Sequence Neural Networks
Authors: Keyang Xu, Zhengzhong Liu, Jamie Callan
Venue: SIGIR
Year: 2017

Many URLs on the Internet point to identical contents, which increase the burden of web crawlers. Techniques that detect such URLs (known as URL de-duping) can greatly save resources such as bandwidth and storage for crawlers. Traditional de-duping methods are usually limited to heavily engineered rule matching strategies.In this work, we propose a novel URL de-duping framework based on sequence-to-sequence (Seq2Seq) neural networks. A single concise translation model can take the place of thousands of explicit rules. Experiments indicate that a vanilla Seq2Seq architecture yields robust and accurate results in detecting duplicate URLs. Furthermore, we demonstrate the efficiency of this framework in the real large-scale web environment.
Cross-Lingual Information Retrieve in Sogou Search
Authors: Jingfang Xu, Feifei Zhai, Zhengshan Xue
Venue: SIGIR
Year: 2017

In recent years, more and more Chinese people desires to be able to access the large amount of foreign language information and understand what is happening all over the world. However, language barrier is always a problem to them. In order to break the language barrier and connect Chinese people to the foreign language information in the world, Sogou has built a cross-lingual information retrieval (CLIR) system named Sogou English (http://english.sogou.com), which enables Chinese people to search and browse foreign language information with Chinese. In Sogou English, when the user inputs a Chinese query, it will first translate the Chinese query into English, and then search over the Internet, and finally translate the search results into Chinese so that users can understand them better. Hence with Sogou English, people can read and browse the information from English world without actually knowing English.
Multi-site User Behavior Modeling and Its Application in Video Recommendation
Authors: Chunfeng Yang, Huan Yan, Donghan Yu, Yong Li, Dah Ming Chiu
Venue: SIGIR
Year: 2017

As online video service continues to grow in popularity, video content providers compete hard for more eyeball engagement. Some users visit multiple video sites to enjoy videos of their interest while some visit exclusively one site. However, due to the isolation of data, mining and exploiting user behaviors in multiple video websites remain unexplored so far. In this work, we try to model user preferences in six popular video websites with user viewing records obtained from a large ISP in China. The empirical study shows that users exhibit both consistent cross-site interests as well as site-specific interests. To represent this dichotomous pattern of user preferences, we propose a generative model of Multi-site Probabilistic Factorization (MPF) to capture both the cross-site as well as site-specific preferences. Besides, we discuss the design principle of our model by analyzing the sources of the observed site-specific user preferences, namely, site peculiarity and data sparsity. Through conducting extensive recommendation validation, we show that our MPF model achieves the best results compared to several other state-of-the-art factorization models with significant improvements of F-measure by 12.96%, 8.24% and 6.88%, respectively. Our findings provide insights on the value of integrating user data from multiple sites, which stimulates collaboration between video service providers.
Video Question Answering via Attribute-Augmented Attention Network Learning
Authors: Yunan Ye, Zhou Zhao, Yimeng Li, Long Chen, Jun Xiao, Yueting Zhuang
Venue: SIGIR
Year: 2017

Video Question Answering is a challenging problem in visual information retrieval, which provides the answer to the referenced video content according to the question. However, the existing visual question answering approaches mainly tackle the problem of static image question, which may be ineffectively for video question answering due to the insufficiency of modeling the temporal dynamics of video contents. In this paper, we study the problem of video question answering by modeling its temporal dynamics with frame-level attention mechanism. We propose the attribute-augmented attention network learning framework that enables the joint frame-level attribute detection and unified video representation learning for video question answering. We then incorporate the multi-step reasoning process for our proposed attention network to further improve the performance. We construct a large-scale video question answering dataset. We conduct the experiments on both multiple-choice and open-ended video question answering tasks to show the effectiveness of the proposed method.
Multi-dimensional Formula Feature Modeling for Mathematical Information Retrieval
Authors: Ke Yuan
Venue: SIGIR
Year: 2017

With the quick availability and growth of mathematical information worldwide, how to effectively retrieve the relevant information about mathematical formulae, has attracted increasing attention from the researchers of mathematical information retrieval (MIR). Existing methods mainly focus on the appearance similarity between formulae. However, there are more important formula-related information that could be explored, for instance, link relations between formulae, formula contexts and temporal information. In this study, I propose a novel formula feature modeling method for mathematical information retrieval. In more details, three new formula features have been proposed for better representing mathematical formulae: formula-related concept features extracted from link structure (Formula Citation Graph, FCG), essential semantic features extracted from descriptive textual information of formulae through Recurrent Neural Networks (RNN) and temporal features extracted from time-related information. All these features could be used to index and retrieve formulae.
Probabilistic Topic Models for Text Data Retrieval and Analysis
Authors: ChengXiang Zhai
Venue: SIGIR
Year: 2017

Text data include all kinds of natural language text such as web pages, news articles, scientific literature, emails, enterprise documents, and social media posts. As text data continues to grow quickly, it is increasingly important to develop intelligent systems to help people manage and make use of vast amounts of text data ("big text data"). As a new family of effective general approaches to text data retrieval and analysis, probabilistic topic models, notably Probabilistic Latent Semantic Analysis (PLSA), Latent Dirichlet Allocations (LDA), and many extensions of them, have been studied actively in the past decade with widespread applications. These topic models are powerful tools for extracting and analyzing latent topics contained in text data; they also provide a general and robust latent semantic representation of text data, thus improving many applications in information retrieval and text mining. Since they are general and robust, they can be applied to text data in any natural language and about any topics. This tutorial will systematically review the major research progress in probabilistic topic models and discuss their applications in text retrieval and text mining. The tutorial will provide (1) an in-depth explanation of the basic concepts, underlying principles, and the two basic topic models (i.e., PLSA and LDA) that have widespread applications, (2) a broad overview of all the major representative topic models (that are usually extensions of PLSA or LDA), and (3) a discussion of major challenges and future research directions. The tutorial should be appealing to anyone who would like to learn about topic models, how and why they work, their widespread applications, and the remaining research challenges to be solved, including especially graduate students, researchers who want to develop new topic models, and practitioners who want to apply topic models to solve many application problems. The attendants are expected to have basic knowledge of probability and statistics.
Hierarchical Community-Level Information Diffusion Modeling in Social Networks
Authors: Yuan Zhang, Tianshu Lyu, Yan Zhang
Venue: SIGIR
Year: 2017

Recently, online social networks are becoming increasingly popular platforms for social interactions. Understanding how information propagates in such networks is important for personalization and recommendation in social search.
Automatically Extracting High-Quality Negative Examples for Answer Selection in Question Answering
Authors: Haotian Zhang, Jinfeng Rao, Jimmy Lin, Mark D. Smucker
Venue: SIGIR
Year: 2017

We propose a heuristic called "one answer per document" for automatically extracting high-quality negative examples for answer selection in question answering. Starting with a collection of question-answer pairs from the popular TrecQA dataset, we identify the original documents from which the answers were drawn. Sentences from these source documents that contain query terms (aside from the answers) are selected as negative examples. Training on the original data plus these negative examples yields improvements in effectiveness by a margin that is comparable to successive recent publications on this dataset. Our technique is completely unsupervised, which means that the gains come essentially for free. We confirm that the improvements can be directly attributed to our heuristic, as other approaches to extracting comparable amounts of training data are not effective. Beyond the empirical validation of this heuristic, we also share our improved TrecQA dataset with the community to support further work in answer selection.
Building Your Own Reading List Anytime via Embedding Relevance, Quality, Timeliness and Diversity
Authors: Bo-Wen Zhang, Xu-Cheng Yin, Fang Zhou, Jian-Lin Jin
Venue: SIGIR
Year: 2017

During every summer holidays, several editions of reading lists are recommended and emerged on mass media, e.g., New York Times, and BBC. However, these reading lists are built for whole people with general topics for some purposes. What if we expect the books of a specific topic at a specific moment? How to generate the requested reading list for our own automatically? In this paper, we propose a searching framework for building a topical reading list anytime, where the Relevance (between topics and books), Quality (of books), Timeliness (of popularities) and Diversity (of results) are embedded into vector representations respectively based on user-generated contents and statistics on social media. We collected 8,197 real-world topics from 198 diverse groups on Librarything.com. The proposed methods are evaluated on the topic collection and the public benchmarks Social Book Search 2012-2016 (SBS). Experimental results demonstrate the robustness and effectiveness of our framework.
Weighted Domain Translation for Online News Comments Emotion Tagging
Authors: Ying Zhang, Li Yu, Xue Zhao, Xiaojie Yuan, Lei Xu
Venue: SIGIR
Year: 2017

This paper studies an emotion classification problem, which aims to classify online news comments to one of fine-grained emotion categories, e.g. happy, sad, and angry, etc. Neural networks have been widely used and achieved great success in sentiment classification. However, there must be sufficient labeled comments available for training neural networks, which usually requires labor-intensive and time-consuming manual labeling. One of the most effective solutions is to apply transfer learning, which uses abundant labeled comments from a source news domain to help the classification for another target domain with limited amount of labeled data. Still, the comments from different domains can have very different word distributions, which makes it difficult to transfer knowledge from one domain to another. In this paper, we accomplish cross-domain emotion tagging based on an advanced neural network BLSTM (bidirectional long short-term memory) with "domain translation'', which can overcome the difference between domains. A weighted linear transformation is utilized to "translate'' knowledge from source to target domain. An extensive set of experimental results on four datasets from popular online news services demonstrates the effectiveness of our proposed models.
Top-K Influential Nodes in Social Networks: A Game Perspective
Authors: Yu Zhang, Yan Zhang
Venue: SIGIR
Year: 2017

Influence maximization, the fundamental of viral marketing, aims to find top-$K$ seed nodes maximizing influence spread under certain spreading models. In this paper, we study influence maximization from a game perspective. We propose a Coordination Game model, in which every individuals make their decisions based on the benefit of coordination with their network neighbors, to study information propagation. Our model serves as the generalization of some existing models, such as Majority Vote model and Linear Threshold model. Under the generalized model, we study the hardness of influence maximization and the approximation guarantee of the greedy algorithm. We also combine several strategies to accelerate the algorithm. Experimental results show that after the acceleration, our algorithm significantly outperforms other heuristics, and it is three orders of magnitude faster than the original greedy method.