OpenRaiser/Intern-Atlas huggingface dataset:
Mining Business Opportunities from Location-based Social Networks
Authors: Shenglin Zhao, Irwin King, Michael R. Lyu, Jia Zeng, Mingxuan Yuan
Venue: SIGIR
Year: 2017

Urbanization's rapid progress has modernized a large number of human beings' lives. This urbanization progress is accompanied by the increase of a variety of shops (e.g., restaurants and fitness centers) to meet the increasing citizens, which means business opportunities for the investors. Nevertheless, it is difficult for the investors to catch such opportunities because opening what kind of business at which place is not easy to decide. In this paper, we take this challenge and define the business opportunity mining problem, which recommends new business categories at a partitioned business district. Specifically, we exploit the data from location-based social networks (LBSNs) to mine the business opportunities, guiding the business owners to open new commercial shops in certain categories at a particular area. First, we define the properties of a business district and propose a greedy algorithm to partition a city into different districts. Next, we propose an embedding model to learn latent representations of categories, which captures the functional correlations among business categories. Furthermore, we propose a ranking model based on the pairwise loss to recommend categories for a specific district. Finally, we conduct experiments on Yelp data, and experimental results show that our proposed method outperforms the baseline methods and resolves the problem well.
Recommending Complementary Products in E-Commerce Push Notifications with a Mixture Model Approach
Authors: Huasha Zhao, Luo Si, Xiaogang Li, Qiong Zhang
Venue: SIGIR
Year: 2017

Push notification is a key component for E-commerce mobile applications, which has been extensively used for user growth and engagement. The effectiveness of the push notification is generally measured by message open rate. A push message can contain a recommended product, a shopping news and etc., but often only one or two items can be shown in the push message due to the limit of display space. This paper proposes a mixture model approach for predicting push message open rate for a post-purchase complementary product recommendation task. The mixture model is trained to learn latent prediction contexts, which are determined by user and item profiles, and then make open rate predictions accordingly. The item with the highest predicted open rate is then chosen to be included in the push notification message for each user. The parameters of the mixture model are optimized using an EM algorithm. A set of experiments are conducted to evaluate the proposed method live with a popular E-Commerce mobile app. The results show that the proposed method is superior than several existing solutions by a significant margin.
Learning Max-Margin GeoSocial Multimedia Network Representations for Point-of-Interest Suggestion
Authors: Zhou Zhao, Qifan Yang, Hanqing Lu, Min Yang, Jun Xiao, Fei Wu, Yueting Zhuang
Venue: SIGIR
Year: 2017

With the rapid development of mobile devices, point-of-interest (POI) suggestion has become a popular online web service, which provides attractive and interesting locations to users. In order to provide interesting POIs, many existing POI recommendation works learn the latent representations of users and POIs from users' past visiting POIs, which suffers from the sparsity problem of POI data. In this paper, we consider the problem of POI suggestion from the viewpoint of learning geosocial multimedia network representations. We propose a novel max-margin metric geosocial multimedia network representation learning framework by exploiting users' check-in behavior and their social relations. We then develop a random-walk based learning method with max-margin metric network embedding. We evaluate the performance of our method on a large-scale geosocial multimedia network dataset and show that our method achieves the best performance than other state-of-the-art solutions.
SIGIR 2017 Tutorial on Health Search (HS2017): A Full-day from Consumers to Clinicians
Authors: Guido Zuccon, Bevan Koopman
Venue: SIGIR
Year: 2017

The HS2017 tutorial will cover topics from an area of information retrieval (IR) with significant societal impact - health search. Whether it is searching patient records, helping medical professionals find best-practice evidence, or helping the public locate reliable and readable health information online, health search is a challenging area for IR research with an actively growing community and many open problems. This tutorial will provide attendees with a full stack of knowledge on health search, from understanding users and their problems to practical, hands-on sessions on current tools and techniques, current campaigns and evaluation resources, as well as important open questions and future directions.
BiNE: Bipartite Network Embedding
Authors: Ming Gao, Leihui Chen, Xiangnan He, Aoying Zhou
Venue: SIGIR
Year: 2018

This work develops a representation learning method for bipartite networks. While existing works have developed various embedding methods for network data, they have primarily focused on homogeneous networks in general and overlooked the special properties of bipartite networks. As such, these methods can be suboptimal for embedding bipartite networks. In this paper, we propose a new method named BiNE, short for Bipartite Network Embedding, to learn the vertex representations for bipartite networks. By performing biased random walks purposefully, we generate vertex sequences that can well preserve the long-tail distribution of vertices in the original bipartite network. We then propose a novel optimization framework by accounting for both the explicit relations (i.e., observed links) and implicit relations (i.e., unobserved but transitive links) in learning the vertex representations. We conduct extensive experiments on several real datasets covering the tasks of link prediction (classification), recommendation (personalized ranking), and visualization. Both quantitative results and qualitative analysis verify the effectiveness and rationality of our BiNE method.
A System for Efficient High-Recall Retrieval
Authors: Mustafa Abualsaud, Nimesh Ghelani, Haotian Zhang, Mark D. Smucker, Gordon V. Cormack, Maura R. Grossman
Venue: SIGIR
Year: 2018

The goal of high-recall information retrieval (HRIR) is to find all or nearly all relevant documents for a search topic. In this paper, we present the design of our system that affords efficient high-recall retrieval. HRIR systems commonly rely on iterative relevance feedback. Our system uses a state-of-the-art implementation of continuous active learning (CAL), and is designed to allow other feedback systems to be attached with little work. Our system allows users to judge documents as fast as possible with no perceptible interface lag. We also support the integration of a search engine for users who would like to interactively search and judge documents. In addition to detailing the design of our system, we report on user feedback collected as part of a 50 participants user study. While we have found that users find the most relevant documents when we restrict user interaction, a majority of participants prefer having flexibility in user interaction. Our work has implications on how to build effective assessment systems and what features of the system are believed to be useful by users.
Extracting Real-Time Insights from Graphs and Social Streams
Authors: Charu C. Aggarwal
Venue: SIGIR
Year: 2018

Many streaming applications in social networks, communication networks, and information networks are built on top of large graphs Such large networks contain continuously occurring processes, which lead to streams of edge interactions and posts. For example, the messages sent by participants on Facebook to one another can be viewed as content-rich interactions along edges. Such edge-centric streams are referred to as graph streams or social streams. The aggregate volume of these interactions can scale up super-linearly with the number of nodes in the network, which makes the problem more pressing for rapidly growing networks. These continuous streams may be mined for useful insights. In these cases, real-time analysis is crucial because of the time-sensitive nature of the interactions. However, generalizing conventional mining applications to such graphs turns out to be a challenge because of the expensive nature of graph mining algorithms. We discuss recent advances in several graph mining applications like clustering, classification, link prediction, event detection, and anomaly detection in real-time graph streams.
Intent-aware Query Obfuscation for Privacy Protection in Personalized Web Search
Authors: Wasi Uddin Ahmad, Kai-Wei Chang, Hongning Wang
Venue: SIGIR
Year: 2018

Modern web search engines exploit users' search history to personalize search results, with a goal of improving their service utility on a per-user basis. But it is this very dimension that leads to the risk of privacy infringement and raises serious public concerns. In this work, we propose a client-centered intent-aware query obfuscation solution for protecting user privacy in a personalized web search scenario. In our solution, each user query is submitted with l additional cover queries and corresponding clicks, which act as decoys to mask users' genuine search intent from a search engine. The cover queries are sequentially sampled from a set of hierarchically organized language models to ensure the coherency of fake search intents in a cover search task. Our approach emphasizes the plausibility of generated cover queries, not only to the current genuine query but also to previous queries in the same task, to increase the complexity for a search engine to identify a user's true intent. We also develop two new metrics from an information theoretic perspective to evaluate the effectiveness of provided privacy protection. Comprehensive experiment comparisons with state-of-the-art query obfuscation techniques are performed on the public AOL search log, and the propitious results substantiate the effectiveness of our solution.
Learning a Deep Listwise Context Model for Ranking Refinement
Authors: Qingyao Ai, Keping Bi, Jiafeng Guo, W. Bruce Croft
Venue: SIGIR
Year: 2018

Learning to rank has been intensively studied and widely applied in information retrieval. Typically, a global ranking function is learned from a set of labeled data, which can achieve good performance on average but may be suboptimal for individual queries by ignoring the fact that relevant documents for different queries may have different distributions in the feature space. Inspired by the idea of pseudo relevance feedback where top ranked documents, which we refer as the local ranking context, can provide important information about the query's characteristics, we propose to use the inherent feature distributions of the top results to learn a Deep Listwise Context Model that helps us fine tune the initial ranked list. Specifically, we employ a recurrent neural network to sequentially encode the top results using their feature vectors, learn a local context model and use it to re-rank the top results. There are three merits with our model: (1) Our model can capture the local ranking context based on the complex interactions between top results using a deep neural network; (2) Our model can be built upon existing learning-to-rank methods by directly using their extracted feature vectors; (3) Our model is trained with an attention-based loss function, which is more effective and efficient than many existing listwise methods. Experimental results show that the proposed model can significantly improve the state-of-the-art learning to rank methods on benchmark retrieval corpora.
Quantitative Information Extraction From Social Data
Authors: Omar Alonso, Thibault Sellam
Venue: SIGIR
Year: 2018

Social data is a rich data source for identifying trends and topics of interest based on user activity. Social data also provides opportunities to collect numerical data about events like elections, sport games, disasters or economic news. We propose the problem of identifying relevant quantitative information from social data as annotations for a topic. We investigate how to extract quantitative information and perform a number of experiments and analysis with Twitter data.
Large-Scale Image Retrieval with Elasticsearch
Authors: Giuseppe Amato, Paolo Bolettieri, Fabio Carrara, Fabrizio Falchi, Claudio Gennaro
Venue: SIGIR
Year: 2018

Content-Based Image Retrieval in large archives through the use of visual features has become a very attractive research topic in recent years. The cause of this strong impulse in this area of research is certainly to be attributed to the use of Convolutional Neural Network (CNN) activations as features and their outstanding performance. However, practically all the available image retrieval systems are implemented in main memory, limiting their applicability and preventing their usage in big-data applications. In this paper, we propose to transform CNN features into textual representations and index them with the well-known full-text retrieval engine Elasticsearch. We validate our approach on a novel CNN feature, namely Regional Maximum Activations of Convolutions. A preliminary experimental evaluation, conducted on the standard benchmark INRIA Holidays, shows the effectiveness and efficiency of the proposed approach and how it compares to state-of-the-art main-memory indexes.
Second International Workshop on Conversational Approaches to Information Retrieval (CAIR'18): Workshop at SIGIR 2018
Authors: Jaime Arguello, Filip Radlinski, Hideo Joho, Damiano Spina, Julia Kiseleva
Venue: SIGIR
Year: 2018

The CAIR'18 workshop will bring together academic and industrial researchers to create a forum for research on conversational approaches to search and recommendation. A specific focus will be on techniques that support complex and multi-turn user-machine dialogues for information access and retrieval, and multi-modal interfaces for interacting with such systems.
Auto-completion for Question Answering Systems at Bloomberg
Authors: Konstantine Arkoudas, Mohamed Yahya
Venue: SIGIR
Year: 2018

The Bloomberg Terminal is the leading source of information and news in the finance industry. Through hundreds of functions that provide access to a vast wealth of structured and semi-structured data, the terminal is able to satisfy a wide range of information needs. Users can find what they need by constructing queries, plotting charts, creating alerts, and so on. Until recently, most queries to the terminal were constructed through dedicated GUIs. For instance, if users wanted to screen for technology companies that met certain criteria, they would specify the criteria by filling out a form via a sequence of interactions with GUI elements such as drop-down lists, checkboxes, radio and toggle buttons, etc. To facilitate information retrieval in the terminal, we are equipping it with the ability to understand and answer queries expressed in natural language. Our QA (question answering) systems map structurally complex questions like the above to a logical meaning representation which can then be translated to an executable query language (such as SQL or SPARQL). At that point we can execute the queries against a suitable back end, obtain the results, and present them to the users. Adding a natural-language interface to a data repository introduces usability challenges of its own, chief amongst them being this: How can the user know what the system can and cannot understand and answer (without needing to undergo extensive training)? We can unpack this question into two separate parts: 1) How can we convey the full range of the system's abilities? 2) How can we convey its limitations? We use auto-complete as a tool to help meet both challenges. Specifically, the first question pertains to the general issue of discoverability: We want at least some of the suggested completions to act as vehicles for discovering data and functionality of which users may have not been previously aware. The second question pertains to expectation management. Naturally, no QA system can attain perfect performance; limiting factors include representational shortcomings and various kinds of incompleteness of the underlying data sources, as well as NLP technology limitations. We want to stop generating completions as a signal indicating that we are not able to understand and/or answer what is being typed.
Citation Worthiness of Sentences in Scientific Reports
Authors: Hamed R. Bonab, Hamed Zamani, Erik G. Learned-Miller, James Allan
Venue: SIGIR
Year: 2018

Does this sentence need citation? In this paper, we introduce the task of citation worthiness for scientific texts at a sentence-level granularity. The task is to detect whether a sentence in a scientific article needs to be cited or not. It can be incorporated into citation recommendation systems to help automate the citation process by marking sentences where needed. It may also be useful for publishers to regularize the citation process. We construct a dataset using the ACL Anthology Reference Corpus; consisting of over 1.1M "not_cite" and 85K "cite" sentences. We study the performance of a set of state-of-the-art sentence classifiers for the citation worthiness task and show the practical challenges. We also explore section-wise difficulty of the task and analyze the performance of our best model on a published article.
From the PRP to the Low Prior Discovery Recall Principle for Recommender Systems
Authors: Rocío Cañamares, Pablo Castells
Venue: SIGIR
Year: 2018

We revisit the Probability Ranking Principle in the context of recommender systems. We find a key difference in the retrieval protocol with respect to query-based search, that leads to the identification of different optimal ranking principles for discovery-oriented recommendation. Based on this finding, we revise the effectiveness of common non-personalized ranking functions in respect to the new principles. We run an experiment confirming and illustrating our theoretical analysis, and providing further observations and hints for reflection and future research.
Attentive Group Recommendation
Authors: Da Cao, Xiangnan He, Lianhai Miao, Yahui An, Chao Yang, Richang Hong
Venue: SIGIR
Year: 2018

Due to the prevalence of group activities in people's daily life, recommending content to a group of users becomes an important task in many information systems. A fundamental problem in group recommendation is how to aggregate the preferences of group members to infer the decision of a group. Toward this end, we contribute a novel solution, namely AGREE (short for ''Attentive Group REcommEndation''), to address the preference aggregation problem by learning the aggregation strategy from data, which is based on the recent developments of attention network and neural collaborative filtering (NCF). Specifically, we adopt an attention mechanism to adapt the representation of a group, and learn the interaction between groups and items from data under the NCF framework. Moreover, since many group recommender systems also have abundant interactions of individual users on items, we further integrate the modeling of user-item interactions into our method. Through this way, we can reinforce the two tasks of recommending items for both groups and users. By experimenting on two real-world datasets, we demonstrate that our AGREE model not only improves the group recommendation performance but also enhances the recommendation for users, especially for cold-start users that have no historical interactions individually.
The Effects of Manipulating Task Determinability on Search Behaviors and Outcomes
Authors: Robert Capra, Jaime Arguello, Heather O&apos, Brien, Yuan Li, Bogeum Choi
Venue: SIGIR
Year: 2018

An important area of IR research involves understanding how task characteristics influence search behaviors and outcomes. Task complexity is one characteristic that has received considerable attention. One view of task complexity is through the lens of a priori determinability -- the level of uncertainty about task outcomes and processes experienced by the searcher. In this work, we manipulated the determinability of comparative tasks. Our task manipulation involved modifying the scope of the task by specifying exact items and/or exact (objective or subjective) dimensions to consider as part of the task. This paper reports on a within-subject study (N=144) where we investigated how our task manipulation influenced participants' perceptions, levels of engagement, search effort, and choice of search strategies. Our results suggest a complex relationship between task scope, determinability, and different outcome measures. Our most open-ended tasks were perceived to have low determinability (high uncertainty), but were the least challenging for participants due to satisficing. Furthermore, narrowing the scope of tasks by specifying items had a different effect than by specifying dimensions. Specifying items increased the task determinability (lower uncertainty) and made the task easier, while specifying dimensions did not increase the task determinability and made the task more challenging. A qualitative analysis of participants' queries suggests that searching for dimensions is more challenging than for items. Finally, we observed subtle differences between objective and subjective dimensions. We discuss implications for the design of IIR studies and tools to support users.
Offline Comparative Evaluation with Incremental, Minimally-Invasive Online Feedback
Authors: Ben Carterette, Praveen Chandar
Venue: SIGIR
Year: 2018

We investigate the use of logged user interaction data---queries and clicks---for offline evaluation of new search systems in the context of counterfactual analysis. The challenge of evaluating a new ranker against log data collected from a static production ranker is that new rankers may retrieve documents that have never been seen in the logs before, and thus lack any logged feedback from users. Additionally, the ranker itself could bias user actions such that even documents that have been seen in the logs would have exhibited different interaction patterns had they been retrieved and ranked by the new ranker. We present a methodology for incrementally logging interactions on previously-unseen documents for use in computation of an unbiased estimator of a new ranker's effectiveness. Our method is very lightly invasive with respect to the production ranker results to insure against users becoming dissatisfied if the new ranker is poor. We demonstrate how well our methods work in a simulation environment designed to be challenging for such methods to argue that they are likely to work in a wide variety of scenarios.
Knowledge Extraction and Inference from Text: Shallow, Deep, and Everything in Between
Authors: Soumen Chakrabarti
Venue: SIGIR
Year: 2018

Systems for structured knowledge extraction and inference have made giant strides in the last decade. Starting from shallow linguistic tagging and coarse-grained recognition of named entities at the resolution of people, places, organizations, and times, modern systems link billions of pages of unstructured text with knowledge graphs having hundreds of millions of entities belonging to tens of thousands of types, and related by tens of thousands of relations. Via deep learning, systems build continuous representations of words, entities, types, and relations, and use these to continually discover new facts to add to the knowledge graph, and support search systems that go far beyond page-level "ten blue links''. We will present a comprehensive catalog of the best practices in traditional and deep knowledge extraction, inference and search. We will trace the development of diverse families of techniques, explore their interrelationships, and point out various loose ends.