OpenRaiser/Intern-Atlas huggingface dataset:
Dynamic location models
Authors: Vanessa Murdock
Venue: SIGIR
Year: 2014

Location models built on social media have been shown to be an important step toward understanding places in queries. Current search technology focuses on predicting broad regions such as cities. Hyperlocal scenarios are important because of the increasing prevalence of smartphones and mobile search and recommendation. Users expect the system to recognize their location and provide information about their immediate surroundings.
ServiceXplorer: a similarity-based web service search engine
Authors: Anne H. H. Ngu, Jiangang Ma, Quan Z. Sheng, Lina Yao, Scott Julian
Venue: SIGIR
Year: 2014

Finding relevant Web services and composing them into value-added applications is becoming increasingly important in cloud and service based marketplaces. The key problem with current approaches to finding relevant Web services is that most of them only provide searches over a discrete set of features using exact keyword matching. We demonstrate in this paper that by utilizing well known indexing scheme such as inverted file and R-tree indexes over Web services attributes, the Earth Mover's Distance (EMD) algorithm can be used efficiently to find partial matches between a query and a database of Web services.
On predicting religion labels in microblogging networks
Authors: Minh Thap Nguyen, Ee-Peng Lim
Venue: SIGIR
Year: 2014

Religious belief plays an important role in how people behave, influencing how they form preferences, interpret events around them, and develop relationships with others. Traditionally, the religion labels of user population are obtained by conducting a large scale census study. Such an approach is both high cost and time consuming. In this paper, we study the problem of predicting users' religion labels using their microblogging data. We formulate religion label prediction as a classification task, and identify content, structure and aggregate features considering their self and social variants for representing a user. We introduce the notion of representative user to identify users who are important in the religious user community. We further define features using representative users. We show that SVM classifiers using our proposed features can accurately assign Christian and Muslim labels to a set of Twitter users with known religion labels.
Weighted aspect-based collaborative filtering
Authors: Yanping Nie, Yang Liu, Xiaohui Yu
Venue: SIGIR
Year: 2014

Existing work on collaborative filtering (CF) is often based on the overall ratings the items have received. However, in many cases, understanding how a user rates each aspect of an item may reveal more detailed information about her preferences and thus may lead to more effective CF. Prior work has studied extracting/quantizing sentiments on different aspects from the reviews, based on which the unknown overall ratings are inferred. However, in that work, all the aspects are treated equally; while in reality, different users tend to place emphases on difference aspects when reaching the overall rating. For example, users may give a high rating to a movie just for its plot despite its mediocre performances. This emphasis on aspects varies for different users and different items. In this paper, we propose a method that uses tensor factorization to automatically infer the weights of different aspects in forming the overall rating. The main idea is to learn, through constrained optimization, a compact representation of a weight tensor indexed by three dimensions for user, item, and aspect, respectively. Overall ratings can then be predicted using the obtained weights. Experiments on a movie dataset show that our method compares favorably with three baseline methods.
What makes data robust: a data analysis in learning to rank
Authors: Shuzi Niu, Yanyan Lan, Jiafeng Guo, Xueqi Cheng, Xiubo Geng
Venue: SIGIR
Year: 2014

When applying learning to rank algorithms in real search applications, noise in human labeled training data becomes an inevitable problem which will affect the performance of the algorithms. Previous work mainly focused on studying how noise affects ranking algorithms and how to design robust ranking algorithms. In our work, we investigate what inherent characteristics make training data robust to label noise. The motivation of our work comes from an interesting observation that a same ranking algorithm may show very different sensitivities to label noise over different data sets. We thus investigate the underlying reason for this observation based on two typical kinds of learning to rank algorithms (i.e.~pairwise and listwise methods) and three different public data sets (i.e.~OHSUMED, TD2003 and MSLR-WEB10K). We find that when label noise increases in training data, it is the \emph{document pair noise ratio} (i.e.~\emph{pNoise}) rather than \emph{document noise ratio} (i.e.~\emph{dNoise}) that can well explain the performance degradation of a ranking algorithm.
Real-time visualization and targeting of online visitors
Authors: Deepak Pai, Sandeep Zechariah George
Venue: SIGIR
Year: 2014

Identifying and targeting visitors on an e-commerce website with personalized content in real-time is extremely important to marketers. Although such targeting exists today, it is based on demographic attributes of the visitors. We show that dynamic visitor attributes extracted from their click-stream provide much better predictive capabilities of visitor intent. In this demonstration, we showcase an interactive real-time user interface for marketers to visualize and target visitor segments. Our dashboard not only provides the marketers understanding of their visitor click patterns, but also lets them target individual or group of visitors with offers and promotions.
Second order probabilistic models for within-document novelty detection in academic articles
Authors: Laurence Anthony F. Park, Simeon Simoff
Venue: SIGIR
Year: 2014

It is becoming increasingly difficult to stay aware of the state-of-the-art in any research field due to the exponential increase in the number of academic publications. This problem effects authors and reviewers of submissions to academic journals and conferences, who must be able to identify which portions of an article are novel and which are not. Therefore, having a process to automatically judge the flow of novelty though a document would assist academics in their quest for truth. In this article, we propose the concept of Within Document Novelty Location, a method of identifying locations of novelty and non-novelty within a given document. In this preliminary investigation, we examine if a second order statistical model has any benefit, in terms of accuracy and confidence, over a simpler first order model. Experiments on 928 text sequences taken from three academic articles showed that the second order model provided a significant increase in novelty location accuracy for two of the three documents. There was no significant difference in accuracy for the remaining document, which is likely to be due to the absence of context analysis.
Reputation analysis with a ranked sentiment-lexicon
Authors: Filipa Peleja, João Santos, João Magalhães
Venue: SIGIR
Year: 2014

Reputation analysis is naturally linked to a sentiment analysis task of the targeted entities. This analysis leverages on a sentiment lexicon that includes general sentiment words and domain specific jargon. However, in most cases target entities are themselves part of the sentiment lexicon, creating a loop from which it is difficult to infer an entity reputation. Sometimes, the entity became a reference in the domain and is vastly cited as an example of a highly reputable entity. For example, in the movies domain it is not uncommon to see reviews citing Batman or Anthony Hopkins as esteemed references. In this paper we describe an unsupervised method for performing a simultaneous-analysis of the reputation of multiple named-entities. Our method jointly extracts named entities reputation and a domain specific sentiment lexicon. The objective is two-fold: (1) named-entities are naturally ranked by our method and (2) we can build a reputation graph of the domain's named entities. This framework has immediate applications in terms of visualization or search by reputation.
The knowing camera 2: recognizing and annotating places-of-interest in smartphone photos
Authors: Pai Peng, Lidan Shou, Ke Chen, Gang Chen, Sai Wu
Venue: SIGIR
Year: 2014

This paper presents a project called Knowing Camera for real-time recognizing and annotating places-of-interest(POI) in smartphone photos, with the availability of online geotagged images of such places. We propose a`"Spatial+Visual" (S+V) framework which consists of a probabilistic field-of-view model in the spatial phase and sparse coding similarity metric in the visual phase to recognize phone-captured POIs. Moreover, we put forward an offline Collaborative Salient Area (COSTAR) mining algorithm to detect common visual features (called Costars) among the noisy photos geotagged on each POI, thus to clean the geotagged image database. The mining result can be utilized to annotate the region-of-interest on the query image during the online query processing. Besides, this mining procedure further improves the efficiency and accuracy of the S+V framework. Our experiments in the real-world and Oxford 5K datasets show promising recognition and annotation performances of the proposed approach, and that the proposed COSTAR mining technique outperforms state-of-the-art approach.
Predicting query performance in microblog retrieval
Authors: Jesus A. Rodriguez Perez, Joemon M. Jose
Venue: SIGIR
Year: 2014

Query Performance Prediction (QPP) is the estimation of the retrieval success for a query, without explicit knowledge about relevant documents. QPP is especially interesting in the context of Automatic Query Expansion (AQE) based on Pseudo Relevance Feedback (PRF). PRF-based AQE is known to produce unreliable results when the initial set of retrieved documents is poor. Theoretically, a good predictor would allow to selectively apply PRF-based AQE when performance of the initial result set is good enough, thus enhancing the overall robustness of the system. QPP would be of great benefit in the context of microblog retrieval, as AQE was the most widely deployed technique for enhancing retrieval performance at TREC. In this work we study the performance of the state of the art predictors under microblog retrieval conditions as well as introducing our own predictors. Our results show how our proposed predictors outperform the baselines significantly.
Score-safe term-dependency processing with hybrid indexes
Authors: Matthias Petri, Alistair Moffat, J. Shane Culpepper
Venue: SIGIR
Year: 2014

Score-safe index processing has received a great deal of attention over the last two decades. By pre-calculating maximum term impacts during indexing, the number of scoring operations can be minimized, and the top-k documents for a query can be located efficiently. However, these methods often ignore the importance of the effectiveness gains possible when using sequential dependency models. We present a hybrid approach which leverages score-safe processing and suffix-based self-indexing structures in order to provide efficient and effective top-k document retrieval.
Co-training on authorship attribution with very fewlabeled examples: methods vs. views
Authors: Tieyun Qian, Bing Liu, Ming Zhong, Guoliang He
Venue: SIGIR
Year: 2014

Authorship attribution (AA) aims to identify the authors of a set of documents. Traditional studies in this area often assume that there are a large set of labeled documents available for training. However, in the real life, it is hard or expensive to collect a large set of labeled data. For example, in the online review domain, most reviewers (authors) only write a few reviews, which are not enough to serve as the training data for accurate classification. In this paper, we present a novel two-view co-training framework to iteratively identify the authors of a few unlabeled data to augment the training set. The key idea is to first represent each document as several distinct views, and then a co-training technique is adopted to exploit the large amount of unlabeled documents. Starting from 10 training texts per author, we systematically evaluate the effectiveness of co-training for authorship attribution with limited labeled data. Two methods and three views are investigated: logistic regression (LR) and support vector machines (SVM) methods, and character, lexical, and syntactic views. The experimental results show that LR is particularly effective for improving co-training in AA, and the lexical view performs the best among three views when combined with a LR classifier. Furthermore, the co-training framework does not make much difference between one classifier from two views and two classifiers from one view. Instead, it is the learning approach and the view that plays a critical role.
A perspective-aware approach to search: visualizing perspectives in news search results
Authors: Muhammad Atif Qureshi, Colm O&apos, Riordan, Gabriella Pasi
Venue: SIGIR
Year: 2014

The result set from a search engine for any user's query may exhibit an inherent perspective due to issues with the search engine or issues with the underlying collection. This demonstration paper presents a system that allows users to specify at query time a perspective together with their query. The system then presents results from well-known search engines with a visualization of the results which allows the users to quickly surmise the presence of the perspective in the returned set.
SearchPanel: framing complex search needs
Authors: Pernilla Qvarfordt, Simon Tretter, Gene Golovchinsky, Tony Dunnigan
Venue: SIGIR
Year: 2014

People often use more than one query when searching for information. They revisit search results to re-find information and build an understanding of their search need through iterative explorations of query formulation. These tasks are not well-supported by search interfaces and web browsers. We designed and built SearchPanel, a Chrome browser extension that supports people in their ongoing information seeking. This extension combines document and process metadata into an interactive representation of the retrieved documents that can be used for sense-making, navigation, and re-finding documents. In a real-world deployment spanning over two months, results show that SearchPanel appears to have been primarily used for complex information needs, in search sessions with long durations and high numbers of queries. When process metadata was present in the UI, searchers in explorative search sessions submitted more and longer queries and interacted more with the SERP. These results indicate that the process metadata features in SearchPanel seem to be of particular importance for exploratory search.
Utilizing relevance feedback in fusion-based retrieval
Authors: Ella Rabinovich, Ofri Rom, Oren Kurland
Venue: SIGIR
Year: 2014

Work on using relevance feedback for retrieval has focused on the single retrieved list setting. That is, an initial document list is retrieved in response to the query and feedback for the most highly ranked documents is used to perform a second search. We address a setting wherein the list for which feedback is provided results from fusing several intermediate retrieved lists. Accordingly, we devise methods that utilize the feedback while exploiting the special characteristics of the fusion setting. Specifically, the feedback serves two different, yet complementary, purposes. The first is to directly rank the pool of documents in the intermediate lists. The second is to estimate the effectiveness of the intermediate lists for improved re-fusion. In addition, we present a meta fusion method that uses the feedback for these two purposes simultaneously. Empirical evaluation demonstrates the merits of our approach. As a case in point, the retrieval performance is substantially better than that of using the relevance feedback as in the single list setting. The performance also substantially transcends that of a previously proposed approach to utilizing relevance feedback in fusion-based retrieval.
Modeling the evolution of product entities
Authors: Priya Radhakrishnan, Manish Gupta, Vasudeva Varma
Venue: SIGIR
Year: 2014

A large number of web queries are related to product entities. Studying evolution of product entities can help analysts understand the change in particular attribute values for these products. However, studying the evolution of a product requires us to be able to link various versions of a product together in a temporal order. While it is easy to temporally link recent versions of products in a few domains manually, solving the problem in general is challenging. The ability to temporally order and link various versions of a single product can also improve product search engines. In this paper, we tackle the problem of finding the previous version (predecessor) of a product entity. Given a repository of product entities, we first parse the product names using a CRF model. After identifying entities corresponding to a single product, we solve the problem of finding the previous version of any given particular version of the product. For the second task, we leverage innovative features with a Naïve Bayes classifier. Our methods achieve a precision of 88% in identifying the product version from product entity names, and a precision of 53% in identifying the predecessor.
The correlation between cluster hypothesis tests and the effectiveness of cluster-based retrieval
Authors: Fiana Raiber, Oren Kurland
Venue: SIGIR
Year: 2014

We present a study of the correlation between the extent to which the cluster hypothesis holds, as measured by various tests, and the relative effectiveness of cluster-based retrieval with respect to document-based retrieval. We show that the correlation can be affected by several factors, such as the size of the result list of the most highly ranked documents that is analyzed. We further show that some cluster hypothesis tests are often negatively correlated with one another. Moreover, in several settings, some of the tests are also negatively correlated with the relative effectiveness of cluster-based retrieval.
Extracting topics based on authors, recipients and content in microblogs
Authors: Nazneen Fatema Rajani, Kate McArdle, Jason Baldridge
Venue: SIGIR
Year: 2014

Microblogs such as Twitter are important sources for spreading vital information at high speed. They also reflect the general people's reaction and opinion towards major events or stories. With information traveling so quickly, it is helpful to be able to apply unsupervised learning techniques to discover topics for information extraction and analysis. Although graphical models have been traditionally used for topic discovery in microblogs and text streams, previous work may not be as efficient because of the diverse and noisy nature of microblogs.
Entity-based retrieval
Authors: Hadas Raviv
Venue: SIGIR
Year: 2014

We address the core challenge of the entity retrieval task: ranking entities in response to a query by their presumed relevance to the information need that the query represents. As an initial research direction we explored two models for entity ranking that were evaluated using the INEX entity ranking dataset and which posted promising performance. A natural future direction to explore is how to generalize these models to address various types of information needs that are associated with entities.
Query performance prediction for entity retrieval
Authors: Hadas Raviv, Oren Kurland, David Carmel
Venue: SIGIR
Year: 2014

We address the query-performance-prediction task for entity retrieval; that is, retrieval effectiveness is estimated with no relevance judgements. First we show how to adapt state-of-the-art query-performance predictors proposed for document retrieval to the entity retrieval domain. We then present a novel predictor that is based on the cluster hypothesis. Evaluation performed with the INEX entity ranking track collections shows that our predictor can often outperform the most effective predictors we experimented with.
Rendering expressions to improve accuracy of relevance assessment for math search
Authors: Matthias S. Reichenbach, Anurag Agarwal, Richard Zanibbi
Venue: SIGIR
Year: 2014

Finding ways to help users assess relevance when they search using math expressions is critical for making Mathematical Information Retrieval (MIR) systems easier to use. We designed a study where participants completed search tasks involving mathematical expressions using two different summary styles, and measured response time and relevance assessment accuracy. The control summary style used Google's regular hit formatting where expressions are presented as text (e.g. in LaTeX), while the second summary style renders the math expressions. Participants were undergraduate and graduate students. Participants in the rendered summary style (n=19) had on average a 17.18% higher assessment accuracy than those in the non-rendered summary style (n=19), with no significant difference in response times. Participants in the rendered condition reported having fewer problems reading hits than participants in the control condition. This suggests that users will benefit from search engines that properly render math expressions in their hit summaries.
Hierarchical multi-label classification of social text streams
Authors: Zhaochun Ren, Maria-Hendrike Peetz, Shangsong Liang, Willemijn van Dolen, Maarten de Rijke
Venue: SIGIR
Year: 2014

Hierarchical multi-label classification assigns a document to multiple hierarchical classes. In this paper we focus on hierarchical multi-label classification of social text streams. Concept drift, complicated relations among classes, and the limited length of documents in social text streams make this a challenging problem. Our approach includes three core ingredients: short document expansion, time-aware topic tracking, and chunk-based structural learning. We extend each short document in social text streams to a more comprehensive representation via state-of-the-art entity linking and sentence ranking strategies. From documents extended in this manner, we infer dynamic probabilistic distributions over topics by dividing topics into dynamic "global" topics and "local" topics. For the third and final phase we propose a chunk-based structural optimization strategy to classify each document into multiple classes. Extensive experiments conducted on a large real-world dataset show the effectiveness of our proposed method for hierarchical multi-label classification of social text streams.
Estimating global statistics for unstructured P2P search in the presence of adversarial peers
Authors: Sami Richardson, Ingemar J. Cox
Venue: SIGIR
Year: 2014

A common problem in unstructured peer-to-peer (P2P) information retrieval is the need to compute global statistics of the full collection, when only a small subset of the collection is visible to a peer. Without accurate estimates of these statistics, the effectiveness of modern retrieval models can be reduced. We show that for the case of a probably approximately correct P2P architecture, and using either the BM25 retrieval model or a language model with Dirichlet smoothing, very close approximations of the required global statistics can be estimated with very little overhead and a small extension to the protocol. However, through theoretical modeling and simulations we demonstrate this technique also greatly increases the ability for adversarial peers to manipulate search results. We show an adversary controlling fewer than 10% of peers can censor or increase the rank of documents, or disrupt overall search results. As a defense, we propose a simple modification to the extension, and show global statistics estimation is viable even when up to 40% of peers are adversarial.