OpenRaiser/Intern-Atlas huggingface dataset:
The Factoid Queries Collection
Authors: Ido Guy, Dan Pelleg
Venue: SIGIR
Year: 2016

We present a collection of over 15,000 queries, issued to commercial web search engines, whose answer is a single fact. The collection was produced based on queries landing on questions within a large community question answering website, each with a best answer no longer than 3 words and an explicit reference to a Wikipedia page. We describe the collection generation process and provide a variety of descriptive characteristics, demonstrating the collection?s uniqueness compared to existing datasets and its potential use for research of factoid question answering and retrieval.
Search as Learning (SAL) Workshop 2016
Authors: Jacek Gwizdka, Preben Hansen, Claudia Hauff, Jiyin He, Noriko Kando
Venue: SIGIR
Year: 2016

The "Search as Learning" (SAL) workshop is focused on an area within the information retrieval field that is only beginning to emerge: supporting users in their learning whilst interacting with information content.
SimCC-AT: A Method to Compute Similarity of Scientific Papers with Automatic Parameter Tuning
Authors: Masoud Reyhani Hamedani, Sang-Wook Kim
Venue: SIGIR
Year: 2016

In this paper, we propose SimCC-AT (similarity based on content and citations with automatic parameter tuning) to compute the similarity of scientific papers. As in SimCC, the state-of-the-art method, we exploit a notion of a contribution score in similarity computation. SimCC-AT utilizes an automatic weighting scheme based on SVMrank and thus requires only a smaller number of experiments for parameter tuning than SimCC. Furthermore, our experimental results with a real-world dataset show that the accuracy of SimCC-AT is dramatically higher than that of other existing methods and is comparable to that of SimCC.
Effective Trend Detection within a Dynamic Search Context
Authors: Anat Hashavit, Roy Levin, Ido Guy, Gilad Kutiel
Venue: SIGIR
Year: 2016

In recent years, studies about trend detection in online social media streams have begun to emerge. Since not all users are likely to always be interested in the same set of trends, some of the research also focused on personalizing the trends by using some predefined personalized context.
SG++: Word Representation with Sentiment and Negation for Twitter Sentiment Classification
Authors: Qinmin Hu, Yijun Pei, Qin Chen, Liang He
Venue: SIGIR
Year: 2016

Here we propose an advance Skip-gram model to incorporate both word sentiment and negation information. In particular, there is a a softmax layer for the word sentiment polarity upon the Skip-gram model. Then, two paralleled embedding layers are set up in the same embedding space, one for the affirmative context and the other for the negated context, followed by their loss functions. We evaluate our proposed model on the 2013 and 2014 SemEval data sets. The experimental results show that the proposed approach achieves better performance and learns higher dimensional word embedding informatively on the large-scale data.
Retweeting Behavior Prediction Based on One-Class Collaborative Filtering in Social Networks
Authors: Bo Jiang, Jiguang Liang, Ying Sha, Rui Li, Wei Liu, Hongyuan Ma, Lihong Wang
Venue: SIGIR
Year: 2016

Social behaviors such as retweetings, comments or likes are valuable information for human activities analysis. We focus here on user's retweeting behavior which has been considered as a key mechanism of information diffusion in social networks. Since we can only observe on which messages user retweet. It is a typically one-class setting which only positive examples or implicit feedback can be observed. However, few research works on retweeting prediction consider one-class setting. In this paper, we analyze and study the fundamental factors that might affect retweetability of a tweet, and then employ one-class collaborative filtering method by quantitatively measure the user personal preference and social influence between users and messages to predict user's retweeting behavior. Experimental results on a real-world dataset from social network show that the proposed method is effective and can improve the performance of the one-class collaborative filtering over baseline methods through leveraging weighted negative examples information.
A Comparison of Cache Blocking Methods for Fast Execution of Ensemble-based Score Computation
Authors: Xin Jin, Tao Yang, Xun Tang
Venue: SIGIR
Year: 2016

Machine-learned classification and ranking techniques often use ensembles to aggregate partial scores of feature vectors for high accuracy and the runtime score computation can become expensive when employing a large number of ensembles. The previous work has shown the judicious use of memory hierarchy in a modern CPU architecture which can effectively shorten the time of score computation. However, different traversal methods and blocking parameter settings can exhibit different cache and cost behavior depending on data and architectural characteristics. It is very time-consuming to conduct exhaustive search for performance comparison and optimum selection. This paper provides an analytic comparison of cache blocking methods on their data access performance with an approximation and proposes a fast guided sampling scheme to select a traversal method and blocking parameters for effective use of memory hierarchy. The evaluation studies with three datasets show that within a reasonable amount of time, the proposed scheme can identify a highly competitive solution that significantly accelerates score calculation.
Counterfactual Evaluation and Learning for Search, Recommendation and Ad Placement
Authors: Thorsten Joachims, Adith Swaminathan
Venue: SIGIR
Year: 2016

Online metrics measured through A/B tests have become the gold standard for many evaluation questions. But can we get the same results as A/B tests without actually fielding a new system? And can we train systems to optimize online metrics without subjecting users to an online learning algorithm? This tutorial summarizes and unifies the emerging body of methods on counterfactual evaluation and learning. These counterfactual techniques provide a well-founded way to evaluate and optimize online metrics by exploiting logs of past user interactions. In particular, the tutorial unifies the causal inference, information retrieval, and machine learning view of this problem, providing the basis for future research in this emerging area of great potential impact. Supplementary material and resources are available online at http://www.cs.cornell.edu/~adith/CfactSIGIR2016.
Building Test Collections for Evaluating Temporal IR
Authors: Hideo Joho, Adam Jatowt, Roi Blanco, Haitao Yu, Shuhei Yamamoto
Venue: SIGIR
Year: 2016

Research on temporal aspects of information retrieval has recently gained considerable interest within the Information Retrieval (IR) community. This paper describes our efforts for building test collections for the purpose of fostering temporal IR research. In particular, we overview the test collections created at the two recent editions of Temporal Information Access (Temporalia) task organized at NTCIR-11 and NTCIR-12, report on selected results and discuss several observations we made during the task design and implementation. Finally, we outline further directions for constructing test collections suitable for temporal IR.
Temporal Information Retrieval
Authors: Nattiya Kanhabua, Avishek Anand
Venue: SIGIR
Year: 2016

The study of temporal dynamics and its impact can be framed within the so-called temporal IR approaches, which explain how user behavior, document content and scale vary with time, and how we can use them in our favor in order to improve retrieval effectiveness. This half-day tutorial will outline research issues with respect to temporal dynamics, and provide a comprehensive overview of temporal IR approaches, essentially regarding processing dynamic content, temporal information extraction, temporal query analysis, and time-aware retrieval and ranking. The tutorial is structured into two sessions. During the first session, we will explain the general and wide aspects associated to temporal dynamics by focusing on the web domain, from content and structural changes to variations of user behavior and interactions. We will begin with temporal indexing and query processing. Next step, we will explain current approaches to time-aware retrieval and ranking, which can be classified into different types based on two main notions of relevance with respect to time, namely, recency-based ranking, and time-dependent ranking. In the latter session, we will describe research issues centered on determining the temporal intent of queries, and time-aware query enhancement, e.g., temporal relevance feedback, and time-aware query reformulation. In addition, we present applications in related research areas, e.g., exploration, summarization, and clustering of search results, as well as future event retrieval and prediction. To this end, we conclude our tutorial and outline future directions. This tutorial targets graduate students, researchers and practitioners in the field of information retrieval. The goal is to provide an overview as well as an important context that enables further research on and practical applications within this area.
Personalised News and Blog Recommendations based on User Location, Facebook and Twitter User Profiling
Authors: Gabriella Kazai, Iskander Yusof, Daoud Clarke
Venue: SIGIR
Year: 2016

This demo presents a prototype mobile app that provides out-of-the-box personalised content recommendations to its users by leveraging and combining the user's location, their Facebook and/or Twitter feed and their in-app actions to automatically infer their interests. We build individual models for each user and each location. At retrieval time we construct the user's personalised feed by mixing different sources of content-based recommendations with content directly from their Facebook/Twitter feeds, locally trending articles and content propagated through their in-app social network. Both explicit and implicit feedback signals from the users' interactions with their recommendations are used to update their interests models and to learn their preferences over the different content sources.
User Interaction in Mobile Web Search
Authors: Jaewon Kim
Venue: SIGIR
Year: 2016

From previous studies, we believe that search behaviour on touch-enabled mobile devices is different from the behaviour with desktop screens. In the proposed research, we intend to explore user interaction while searching with the aim of improving search experience on mobile devices.
Load-Balancing in Distributed Selective Search
Authors: Yubin Kim, Jamie Callan, J. Shane Culpepper, Alistair Moffat
Venue: SIGIR
Year: 2016

Simulation and analysis have shown that selective search can reduce the cost of large-scale distributed information retrieval. By partitioning the collection into small topical shards, and then using a resource ranking algorithm to choose a subset of shards to search for each query, fewer postings are evaluated. Here we extend the study of selective search using a fine-grained simulation investigating: selective search efficiency in a parallel query processing environment; the difference in efficiency when term-based and sample-based resource selection algorithms are used; and the effect of two policies for assigning index shards to machines. Results obtained for two large datasets and four large query logs confirm that selective search is significantly more efficient than conventional distributed search. In particular, we show that selective search is capable of both higher throughput and lower latency in a parallel environment than is exhaustive search.
A Test Collection for Matching Patients to Clinical Trials
Authors: Bevan Koopman, Guido Zuccon
Venue: SIGIR
Year: 2016

We present a test collection to study the use of search engines for matching eligible patients (the query) to clinical trials (the document). Clinical trials are experiments conducted in the development of new medical treatments, drugs or devices. Recruiting candidates for a trial is often a time-consuming and resource intensive effort, and imposes delays or even the cancellation of trials.
Big Data in Climate: Opportunities and Challenges for Machine Learning
Authors: Vipin Kumar
Venue: SIGIR
Year: 2016

This talk will present an overview of research being done in a large interdisciplinary project on the development of novel data mining and machine learning approaches for analyzing massive amount of climate and ecosystem data now available from satellite and ground-based sensors, and physics-based climate model simulations. These information-rich data sets offer huge potential for monitoring, understanding, and predicting the behavior of the Earth's ecosystem and for advancing the science of global change. This talk will discuss challenges in analyzing such data sets and some of our research results in mapping the dynamics of surface water globally as well as detecting deforestation and fires in tropical forests using data from Earth observing satellites.
A Novel Approach to Define and Model Contextual Features in Recommender Systems
Authors: Parisa Lak
Venue: SIGIR
Year: 2016

Recommender Systems(RS) provide more accurate and more relevant recommendations using contextual feature(s). This accuracy improvement is at the cost of computational expenses. Therefore, finding and selecting the most relevant contextual features is an important problem. Moreover, modeling and incorporating the selected contextual features in RS algorithms has an impact on both the accuracy and computational cost. We are conducting a series of studies to detect, define, select, model and incorporate the most relevant contextual features for RS algorithms. The feature detection, definition and selection approach involves the evaluation of features derived from implicit and explicit information. The selected features from this approach can be modeled and incorporated in any selected RS algorithm.
Quote Recommendation in Dialogue using Deep Neural Network
Authors: Hanbit Lee, Yeonchan Ahn, Haejun Lee, Seungdo Ha, Sang-goo Lee
Venue: SIGIR
Year: 2016

Quotes, or quotations, are well known phrases or sentences that we use for various purposes such as emphasis, elaboration, and humor. In this paper, we introduce a task of recommending quotes which are suitable for given dialogue context and we present a deep learning recommender system which combines recurrent neural network and convolutional neural network in order to learn semantic representation of each utterance and construct a sequence model for the dialog thread. We collected a large set of twitter dialogues with quote occurrences in order to evaluate proposed recommender system. Experimental results show that our approach outperforms not only the other state-of-the-art algorithms in quote recommendation task, but also other neural network based methods built for similar tasks.
Collaborative Filtering Bandits
Authors: Shuai Li, Alexandros Karatzoglou, Claudio Gentile
Venue: SIGIR
Year: 2016

Classical collaborative filtering, and content-based filtering methods try to learn a static recommendation model given training data. These approaches are far from ideal in highly dynamic recommendation domains such as news recommendation and computational advertisement, where the set of items and users is very fluid. In this work, we investigate an adaptive clustering technique for content recommendation based on exploration-exploitation strategies in contextual multi-armed bandit settings. Our algorithm takes into account the collaborative effects that arise due to the interaction of the users with the items, by dynamically grouping users based on the items under consideration and, at the same time, grouping items based on the similarity of the clusterings induced over the users. The resulting algorithm thus takes advantage of preference patterns in the data in a way akin to collaborative filtering methods. We provide an empirical analysis on medium-size real-world datasets, showing scalability and increased prediction performance (as measured by click-through rate) over state-of-the-art methods for clustering bandits. We also provide a regret analysis within a standard linear stochastic noise setting.
Deep Learning for Information Retrieval
Authors: Hang Li, Zhengdong Lu
Venue: SIGIR
Year: 2016

Recent years have observed a significant progress in information retrieval and natural language processing with deep learning technologies being successfully applied into almost all of their major tasks. The key to the success of deep learning is its capability of accurately learning distributed representations (vector representations or structured arrangement of them) of natural language expressions such as sentences, and effectively utilizing the representations in the tasks. This tutorial aims at summarizing and introducing the results of recent research on deep learning for information retrieval, in order to stimulate and foster more significant research and development work on the topic in the future.
Fairness in Information Retrieval
Authors: Aldo Lipani
Venue: SIGIR
Year: 2016

The offline evaluation of Information Retrieval (IR) systems is performed through the use of test collections. A test collection, in its essence, is composed of: a collection of documents, a set of topics and, a set of relevance assessments for each topic, derived from the collection of documents. Ideally, for each topic, all the documents of the test collection should be judged, but due to the dimensions of the collections of documents, and their exponential growth over the years, this practice soon became impractical. Therefore, early in IR history, this problem has been addressed through the use of the pooling method. The pooling method consists of optimizing the relevance assessment process by pooling the documents retrieved by different search engines following a particular pooling strategy. The most common one consists on pooling the top d documents of each run. The pool is constructed from systems taking part in a challenge for which the collection was made, at a specific point in time, after which the collection is generally frozen in terms of relevance judgments. This method leads to a bias called pool bias, which is the effect that documents that were not selected in the pool created from the original runs will never be considered relevant. Thereby, this bias affects the evaluation of a system that has not been part of the pool, with any IR evaluation measures, making the comparison with pooled systems unfair.
The ComeWithMe System for Searching and Ranking Activity-Based Carpooling Rides
Authors: Vinicius Monteiro de Lira, Chiara Renso, Raffaele Perego, Salvatore Rinzivillo, Valéria Cesário Times
Venue: SIGIR
Year: 2016

ComeWithMe is an activity oriented carpooling service that enlarges the candidate destinations of a ride request by considering alternative places where the desired activity can be performed. It is based on the observation that individuals often move towards a place to perform an activity while the activity is often not strictly associated with a single place, as one may go for shopping or eating to many different locations. Activity-oriented carpooling hugely increases the number of rides matching a query, thus introducing requirements on system responsiveness and ranking effectiveness that are not common to traditional carpooling services. The demoed system implements the ComeWithMe service in almost its entirety, and includes the back-end and a user-friendly mobile application for smart-phones aimed at achieving users' acceptance and usability.
Retrieving Non-Redundant Questions to Summarize a Product Review
Authors: Mengwen Liu, Yi Fang, Dae Hoon Park, Xiaohua Hu, Zhengtao Yu
Venue: SIGIR
Year: 2016

Product reviews have become an important resource for customers before they make purchase decisions. However, the abundance of reviews makes it difficult for customers to digest them and make informed choices. In our study, we aim to help customers who want to quickly capture the main idea of a lengthy product review before they read the details. In contrast with existing work on review analysis and document summarization, we aim to retrieve a set of real-world user questions to summarize a review. In this way, users would know what questions a given review can address and they may further read the review only if they have similar questions about the product. Specifically, we design a two-stage approach which consists of question retrieval and question diversification. We first propose probabilistic retrieval models to locate candidate questions that are relevant to a review. We then design a set function to re-rank the questions with the goal of rewarding diversity in the final question set. The set function satisfies submodularity and monotonicity, which results in an efficient greedy algorithm of submodular optimization. Evaluation on product reviews from two categories shows that the proposed approach is effective for discovering meaningful questions that are representative for individual reviews.
Composite Correlation Quantization for Efficient Multimodal Retrieval
Authors: Mingsheng Long, Yue Cao, Jianmin Wang, Philip S. Yu
Venue: SIGIR
Year: 2016

Efficient similarity retrieval from large-scale multimodal database is pervasive in modern search engines and social networks. To support queries across content modalities, the system should enable cross-modal correlation and computation-efficient indexing. While hashing methods have shown great potential in achieving this goal, current attempts generally fail to learn isomorphic hash codes in a seamless scheme, that is, they embed multiple modalities in a continuous isomorphic space and separately threshold embeddings into binary codes, which incurs substantial loss of retrieval accuracy. In this paper, we approach seamless multimodal hashing by proposing a novel Composite Correlation Quantization (CCQ) model. Specifically, CCQ jointly finds correlation-maximal mappings that transform different modalities into isomorphic latent space, and learns composite quantizers that convert the isomorphic latent features into compact binary codes. An optimization framework is devised to preserve both intra-modal similarity and inter-modal correlation through minimizing both reconstruction and quantization errors, which can be trained from both paired and partially paired data in linear time. A comprehensive set of experiments clearly show the superior effectiveness and efficiency of CCQ against the state of the art hashing methods for both unimodal and cross-modal retrieval.
Computational Creativity Based Video Recommendation
Authors: Wei Lu, Korris Fu-Lai Chung
Venue: SIGIR
Year: 2016

Computational creativity, as an emerging domain of application, emphasizes the use of big data to automatically design new knowledge. Based on the availability of complex multi-relational data, one aspect of computational creativity is to infer unexplored regions of feature space and novel learning paradigm, which is particularly useful for online recommendation. Tensor models offer effective approaches for complex multi-relational data learning and missing element completion. Targeting at constructing a recommender system that can compromise between accuracy and creativity for users, a deep Bayesian probabilistic tensor framework for tag and item recommending is adopted. Empirical results demonstrate the superiority of the proposed method and indicate that it can better capture latent patterns of interaction relationships and generate interesting recommendations based on creative tag combinations.