OpenRaiser/Intern-Atlas huggingface dataset:
Directed Edge Recommender System
Authors: Ios Kotsogiannis, Elena Zheleva, Ashwin Machanavajjhala
Venue: WSDM
Year: 2017

Recommender systems have become ubiquitous in online applications where companies personalize the user experience based on explicit or inferred user preferences. Most modern recommender systems concentrate on finding relevant items for each individual user. In this paper, we describe the problem of directed edge recommendations where the system recommends the best item that a user can gift, share or recommend to another user that he/she is connected to. We propose algorithms that utilize the preferences of both the sender and the recipient by integrating individual user preference models (e.g., based on items each user purchased for themselves) with models of sharing preferences (e.g., gift purchases for others) into the recommendation process. We compare our work to group recommender systems and social network edge labeling, showing that incorporating the task context leads to more accurate recommendations.
Related Event Discovery
Authors: Cheng Li, Michael Bendersky, Vijay Garg, Sujith Ravi
Venue: WSDM
Year: 2017

We consider the problem of discovering local events on the web, where events are entities extracted from webpages. Examples of such local events include small venue concerts, farmers markets, sports activities, etc. Given an event entity, we propose a graph-based framework for retrieving a ranked list of related events that a user is likely to be interested in attending. Due to the difficulty of obtaining ground-truth labels for event entities, which are temporal and are constrained by location, our retrieval framework is unsupervised, and its graph-based formulation addresses (a) the challenge of feature sparseness and noisiness, and (b) the semantic mismatch problem in a self-contained and principled manner.
Applying Space Syntax to Online Mapping Tools
Authors: Yandi Li, Nicola Barbieri, Daniele Quercia
Venue: WSDM
Year: 2017

To walk around the city, individuals use mobile mapping services, and such services mostly suggest shortest routes. To go beyond recommending such walkable routes, we propose a new framework for automatic wayfinding for pedestrians. This framework tackles two main drawbacks from which past work suffers, namely coarse-grained representation of space and absence of contextual dynamics. We model the human tendency to regularize space by borrowing a spatial representation, Space Syntax, from the discipline of Architecture. Moreover, the proposed framework accounts for contextual dynamics of individual streets by predicting the popularity of each street under different contexts (e.g., at a given time, with a certain weather condition). Using Foursquare check-ins (i.e., whereabouts of the users of the popular location-based service) and publicly available weather data, we validate our framework in the entire city of Barcelona. We find that, with paths slightly longer than the shortest ones, our framework is able to accommodate our mental topography and effectively capture contextual changes.
Learning Parametric Models for Context-Aware Query Auto-Completion via Hawkes Processes
Authors: Liangda Li, Hongbo Deng, Jianhui Chen, Yi Chang
Venue: WSDM
Year: 2017

Query auto completion (QAC) is a prominent feature in modern search engines. High quality QAC substantially improves search experiences by helping users in typing less while submitting the queries. Many studies have been proposed to improve quality and relevance of the QAC methods from different perspectives, including leveraging contexts in long term and short term query histories, investigating the temporal information for time-sensitive QAC, and analyzing user behaviors. Although these studies have shown the context, temporal, and user behavior data carry valuable information, most existing QAC approaches do not fully exploit or even completely ignore these information. We propose a novel Hawkes process based QAC algorithm, comprehensively taking into account the context, temporal, and position of the clicked recommended query completions (a type of user behavior data), for reliable query completion prediction. Our understanding of ranking query completions is consistent with the mathematical rationale of Hawke process; such a coincidence in turn validates our motivation of using Hawkes process for QAC. We also develop an efficient inference algorithm to compute the optimal solutions of the proposed QAC algorithm. The proposed method is evaluated on two real-world benchmark data in comparison with state-of-art methods, and the obtained experiments clearly demonstrate their effectiveness.
Deep Memory Networks for Attitude Identification
Authors: Cheng Li, Xiaoxiao Guo, Qiaozhu Mei
Venue: WSDM
Year: 2017

We consider the task of identifying attitudes towards a given set of entities from text. Conventionally, this task is decomposed into two separate subtasks: target detection that identifies whether each entity is mentioned in the text, either explicitly or implicitly, and polarity classification that classifies the exact sentiment towards an identified entity (the target) into positive, negative, or neutral. Instead, we show that attitude identification can be solved with an end-to-end machine learning architecture, in which the two subtasks are interleaved by a deep memory network. In this way, signals produced in target detection provide clues for polarity classification, and reversely, the predicted polarity provides feedback to the identification of targets. Moreover, the treatments for the set of targets also influence each other -- the learned representations may share the same semantics for some targets but vary for others. The proposed deep memory network, the AttNet, outperforms methods that do not consider the interactions between the subtasks or those among the targets, including conventional machine learning methods and the state-of-the-art deep learning models.
Investigation of User Search Behavior While Facing Heterogeneous Search Services
Authors: Xin Li, Yiqun Liu, Rongjie Cai, Shaoping Ma
Venue: WSDM
Year: 2017

With Web users' search tasks becoming increasingly complex, a single information source cannot necessarily satisfy their information needs. Searchers may rely on heterogeneous sources to complete their tasks, such as search engines, Community Question Answering (CQA), encyclopedia sites, and crowdsourcing platforms. Previous works focus on interaction behaviors with federated search results, including how to compose a federated Web search result page and what factors affect users' interaction behavior on aggregated search interfaces. However, little is known about which factors are crucial in determining users' search outcomes while facing multiple heterogeneous search services. In this paper, we design a lab-based user study to analyze what explicit and implicit factors affect search outcomes (information gain and user satisfaction) when users have access to heterogeneous information sources. In the study, each participant can access three different kinds of search services: a general search engine (Bing), a general CQA portal (Baidu Knows), and a high-quality CQA portal (Zhihu). Using questionnaires and interaction log data, we extract explicit and implicit signals to analyze how users' search outcomes are correlated with their behaviors on different information sources. Experimental results indicate that users' search experiences on CQA portals (such as users' perceived usefulness and number of result clicks) positively affect search outcome (information gain), while search satisfaction is significantly correlated with some other factors such as users' familiarity, interest and difficulty of the task. Besides, users' search satisfaction can be more accurately predicted by the implicit factors than search outcomes.
A Cost Model for Long-Term Compressed Data Retention
Authors: Kewen Liao, Alistair Moffat, Matthias Petri, Anthony Wirth
Venue: WSDM
Year: 2017

Vast amounts of data are collected and stored every day, as part of corporate knowledge bases and as a response to legislative compliance requirements. To reduce the cost of retaining such data, compression tools are often applied. But simply seeking the best compression ratio is not necessarily the most economical choice, and other factors also come in to play, including compression and decompression throughput, the main memory required to support a given level of on-going access to the stored data, and the types of storage available. Here we develop a model for the total retention cost (TRC) of a data archiving regime, and by applying the charging rates associated with a cloud computing provider, are able to derive dollar amounts for a range of compression options, and hence guide the development of new approaches that are more cost-effective than current mechanisms. In particular, we describe an enhancement to the Relative Lempel Ziv (RLZ) compression scheme, and show that in terms of TRC, it outperforms previous approaches in terms of providing economical long-term data retention.
Social Media Anomaly Detection: Challenges and Solutions
Authors: Yan Liu, Sanjay Chawla
Venue: WSDM
Year: 2017

Anomaly detection is of critical importance to prevent malicious activities such as bullying, terrorist attack planning, and fraud information dissemination. With the recent popularity of social media, new types of anomalous behaviors arise, causing concerns from various parties. While a large body of work haven been dedicated to traditional anomaly detection problems, we observe a surge of research interests in the new realm of social media anomaly detection. In this tutorial, we survey existing work on social media anomaly detection, focusing on the new anomalous phenomena in social media and most recent techniques to detect those special types of anomalies. We aim to provide a general overview of the problem domain, common formulations, existing methodologies and future directions.
Semantic-aware Query Processing for Activity Trajectories
Authors: Huiwen Liu, Jiajie Xu, Kai Zheng, Chengfei Liu, Lan Du, Xian Wu
Venue: WSDM
Year: 2017

Nowadays, users of social networks like tweets and weibo have generated massive geo-tagged records, and these records reveal their activities in the physical world together with spatio-temporal dynamics. Existing trajectory data management studies mainly focus on analyzing the spatio-temporal properties of trajectories, while leaving the understanding of their activities largely untouched. In this paper, we incorporate the semantic analysis of the activity information embedded in trajectories into query modelling and processing, with the aim of providing end users more accurate and meaningful trip recommendations. To this end, we propose a novel trajectory query that not only considers the spatio-temporal closeness but also, more importantly, leverages probabilistic topic modelling to capture the semantic relevance of the activities between data and query. To support efficient query processing, we design a novel hybrid index structure, namely ST-tree, to organize the trajectory points hierarchically, which enables us to prune the search space in spatial and topic dimensions simultaneously. The experimental results on real datasets demonstrate the efficiency and scalability of the proposed index structure and search algorithms.
Does Document Relevance Affect the Searcher's Perception of Time?
Authors: Cheng Luo, Yiqun Liu, Tetsuya Sakai, Ke Zhou, Fan Zhang, Xue Li, Shaoping Ma
Venue: WSDM
Year: 2017

Time plays an essential role in multiple areas of Information Retrieval (IR) studies such as search evaluation, user behavior analysis, temporal search result ranking and query understanding. Especially, in search evaluation studies, time is usually adopted as a measure to quantify users' efforts in search processes. Psychological studies have reported that the time perception of human beings can be affected by many stimuli, such as attention and motivation, which are closely related to many cognitive factors in search. Considering the fact that users' search experiences are affected by their subjective feelings of time, rather than the objective time measured by timing devices, it is necessary to look into the different factors that have impacts on search users' perception of time. In this work, we make a first step towards revealing the time perception mechanism of search users with the following contributions: (1) We establish an experimental research framework to measure the subjective perception of time while reading documents in search scenario, which originates from but is also different from traditional time perception measurements in psychological studies. (2) With the framework, we show that while users are reading result documents, document relevance has small yet visible effect on search users' perception of time. By further examining the impact of other factors, we demonstrate that the effect on relevant documents can also be influenced by individuals and tasks. (3) We conduct a preliminary experiment in which the difference between perceived time and dwell time is taken into consideration in a search evaluation task. We found that the revised framework achieved a better correlation with users' satisfaction feedbacks. This work may help us better understand the time perception mechanism of search users and provide insights in how to better incorporate time factor in search evaluation studies.
Adapting Information Retrieval to User Signals via Stochastic Models
Authors: Maria Maistro
Venue: WSDM
Year: 2017

To address the challenge of adapting Information Retrieval (IR) to the constantly evolving user tasks and needs and to adjust it to user interactions and preferences we develop a new model of user behavior based on Markov chains. We aim at integrating the proposed model into several aspects of IR, i.e. evaluation measures, systems and collections. Firstly, we studied IR evaluation measures and we propose a theoretical framework to describe their properties. Then, we presented a new family of evaluation measures, called Markov Precision (MP), based on the proposed model and able to explicitly link lab-style and on-line evaluation metrics. Future work will include the presented model into Learning to Rank (LtR) algorithms and will define a collection for evaluation and comparison of Personalized Information Retrieval (PIR) systems.
Beyond Query Logs: Recommendation and Evaluation
Authors: Matthew Ryan Mitsui
Venue: WSDM
Year: 2017

Query recommendation in Web search is typically manifested in algorithms that 1) recommend previously issued queries from a query log or 2) make incremental changes to queries in a user's current session. While such approaches have been effective in improving retrieval, they either are limited to suggesting queries in a query log or fail to make appropriate leaps that are necessary for query recommendation. More crucially, these approaches only recommend queries that are a coarse approximation of the information a user needs to complete their goal. They do not directly attempt to model the need and generate recommendations from it. This work will propose a framework for generating novel yet focused queries for query recommendation.
Multi-Column Convolutional Neural Networks with Causality-Attention for Why-Question Answering
Authors: Jong-Hoon Oh, Kentaro Torisawa, Canasai Kruengkrai, Ryu Iida, Julien Kloetzer
Venue: WSDM
Year: 2017

Why-question answering (why-QA) is a task to retrieve answers (or answer passages) to why-questions (e.g., "why are tsunamis generated?") from a text archive. Several previously proposed methods for why-QA improved their performance by automatically recognizing causalities that are expressed with such explicit cues as "because" in answer passages and using the recognized causalities as a clue for finding proper answers. However, in answer passages, causalities might be implicitly expressed, (i.e., without any explicit cues): "An earthquake suddenly displaced sea water and a tsunami was generated." The previous works did not deal with such implicitly expressed causalities and failed to find proper answers that included the causalities. We improve why-QA based on the following two ideas. First, implicitly expressed causalities in one text might be expressed in other texts with explicit cues. If we can automatically recognize such explicitly expressed causalities from a text archive and use them to complement the implicitly expressed causalities in an answer passage, we can improve why-QA. Second, the causes of similar events tend to be described with a similar set of words (e.g., "seismic energy" and "tectonic plates" for "the Great East Japan Earthquake" and "the 1906 San Francisco Earthquake"). As such, even if we cannot find in a text archive any explicitly expressed cause of an event (e.g., "the Great East Japan Earthquake") expressed in a question (e.g., "Why did the Great East Japan earthquake happen?"), we might be able to identify its implicitly expressed causes with a set of words (e.g., "tectonic plates") that appear in the explicitly expressed cause of a similar event (e.g., "the 1906 San Francisco Earthquake").
Raising Graphs From Randomness to Reveal Information Networks
Authors: Róbert Pálovics, András A. Benczúr
Venue: WSDM
Year: 2017

We analyze the fine-grained connections between the average degree and the power-law degree distribution exponent in growing information networks. Our starting observation is a power-law degree distribution with a decreasing exponent and increasing average degree as a function of the network size. Our experiments are based on three Twitter at-mention networks and three more from the Koblenz Network Collection. We observe that popular network models cannot explain decreasing power-law degree distribution exponent and increasing average degree at the same time. We propose a model that is the combination of exponential growth, and a power-law developing network, in which new "homophily" edges are continuously added to nodes proportional to their current homophily degree. Parameters of the average degree growth and the power-law degree distribution exponent functions depend on the ratio of the network growth exponent parameters. Specifically, we connect the growth of the average degree to the decreasing exponent of the power-law degree distribution. Prior to our work, only one of the two cases were handled. Existing models and even their combinations can only reproduce some of our key new observations in growing information networks.
Motifs in Temporal Networks
Authors: Ashwin Paranjape, Austin R. Benson, Jure Leskovec
Venue: WSDM
Year: 2017

Networks are a fundamental tool for modeling complex systems in a variety of domains including social and communication networks as well as biology and neuroscience. The counts of small subgraph patterns in networks, called network motifs, are crucial to understanding the structure and function of these systems. However, the role of network motifs for temporal networks, which contain many timestamped links between nodes, is not well understood. Here we develop a notion of a temporal network motif as an elementary unit of temporal networks and provide a general methodology for counting such motifs. We define temporal network motifs as induced subgraphs on sequences of edges, design several fast algorithms for counting temporal network motifs, and prove their runtime complexity. We also show that our fast algorithms achieve 1.3x to 56.5x speedups compared to a baseline method. We use our algorithms to count temporal network motifs in a variety of real-world datasets. Results show that networks from different domains have significantly different motif frequencies, whereas networks from the same domain tend to have similar motif frequencies. We also find that measuring motif counts at various time scales reveals different behavior.
German Typographers vs. German Grammar: Decomposition of Wikipedia Category Labels into Attribute-Value Pairs
Authors: Marius Pasca
Venue: WSDM
Year: 2017

Given an instance (Julieta Pinto), most methods for open-domain information extraction focus on acquiring knowledge in the form of either class labels (Costa Rican short story writers, Women novelists) referring to concepts to which the instance belongs; or facts (nationality: Costa Rica) connecting the instance (Julieta Pinto) to other instances or concepts (Costa Rica), where the fact and the other instance often take the form of an attribute (nationality) and a value (Costa Rica) respectively. From extraction through internal representation and storage, class labels and facts are treated as if they carved out disconnected slices within the larger space of factual knowledge. This paper argues that class labels and facts pertaining to an instance exist in symbiosis rather than as a dichotomy. A constituent (Costa Rican) within a class label (Costa Rican short story writers) of an instance may be indicative of a fact (nationality: Costa Rica) applicable to the instance and vice-versa. As an illustration of the relationship between class labels and facts, the paper introduces an open-domain method for the better understanding of the semantics of class labels in one of the larger and most widely-used repositories of knowledge, namely the categories in the Wikipedia category network. The method exploits the category network to associate constituents (Costa Rican) within names of Wikipedia categories, with attributes (nationality) that explain their role.
Workshop on Scholarly Web Mining (SWM 2017)
Authors: Robert M. Patton, Thomas E. Potok, Petr Knoth, Drahomira Herrmannova
Venue: WSDM
Year: 2017

Researchers increasingly report their results through online publications, from research papers, data and software to experiments, observations and ideas. Immense amount of research-related data is available on the web on interlinked pages, in repositories, databases, social networking sites, etc. Consequently, researchers rely on online sources, often through search engines, to perform literature searches for their research âĂŤ to search for papers, topics, people etc. to be able to produce new research. However, these publications can be used not only for traditional literature searches, but also as a source for discovering popular and emerging research topics, key publications and people or evaluating research excellence. To aid research, it is important to leverage the potential of data mining technologies to improve the process of how research is being done. This workshop aims to bring together people from different backgrounds who are interested in analysing and mining scholarly data available via web and social media sources using various approaches such as query log mining, graph analysis, text mining, etc., and/or who develop systems that enable such analysis and mining. The topics of this workshop include, but are not limited to, the following areas:
Bartering Books to Beers: A Recommender System for Exchange Platforms
Authors: Jérémie Rappaz, Maria-Luiza Vladarean, Julian J. McAuley, Michele Catasta
Venue: WSDM
Year: 2017

Bartering is a timeless practice that is becoming increasingly popular on the Web. Recommending trades for an online bartering platform shares many similarities with traditional approaches to recommendation, in particular the need to model the preferences of users and the properties of the items they consume. However, there are several aspects that make bartering problems interesting and challenging, specifically the fact that users are both suppliers and consumers, and that the trading environment is highly dynamic. Thus, a successful model of bartering requires us to understand not just users' preferences, but also the social dynamics of who trades with whom, and the temporal dynamics of when trades occur.