OpenRaiser/Intern-Atlas huggingface dataset:
Collaborative filtering with short term preferences mining
Authors: Diyi Yang, Tianqi Chen, Weinan Zhang, Yong Yu
Venue: SIGIR
Year: 2012

Recently, recommender systems have fascinated researchers and benefited a variety of people's online activities, enabling users to survive the explosive web information. Traditional collaborative filtering techniques handle the general recommendation well. However, most such approaches usually focus on long term preferences. To discover more short term factors influencing people's decisions, we propose a short term preferences model, implemented with implicit user feedback. We conduct experiments comparing the performances of different short term models, which show that our model outperforms significantly compared to those long term models.
A hybrid model for ad-hoc information retrieval
Authors: Zheng Ye, Jimmy Xiangji Huang, Jun Miao
Venue: SIGIR
Year: 2012

Many information retrieval (IR) techniques have been proposed to improve the performance, and some combinations of these techniques has been demonstrated to be effective. However, how to effectively combine them is largely unexplored. It is possible that a method reduces the positive influence of the other one even if both of them are effective separately. In this paper, we propose a new hybrid model which can simply and flexibly combine components of three different IR techniques under a uniform framework. Extensive experiments on the TREC standard collections indicate that our proposed model can outperform the best TREC systems consistently in the ad-hoc retrieval. It shows that the combination strategy in our proposed model is very effective. Meanwhile, this method is also re-useable for other researchers to test whether their new methods are additive to the current technologies.
On judgments obtained from a commercial search engine
Authors: Emine Yilmaz, Gabriella Kazai, Nick Craswell, Seyed M. M. Tahaghoghi
Venue: SIGIR
Year: 2012

In information retrieval, relevance judgments play an important role as they are required both for evaluating the quality of retrieval systems and for training learning to rank algorithms. In recent years, numerous papers have been published using judgments obtained from a commercial search engine by researchers in industry. As typically no information is provided about the quality of these judgments, their reliability for evaluating/training retrieval systems remains questionable. In this paper, we analyze the reliability of such judgments for evaluating the quality of retrieval systems by comparing them to judgments by NIST judges at TREC.
SimFusion+: extending simfusion towards efficient estimation on large and dynamic networks
Authors: Weiren Yu, Xuemin Lin, Wenjie Zhang, Ying Zhang, Jiajin Le
Venue: SIGIR
Year: 2012

SimFusion has become a captivating measure of similarity between objects in a web graph. It is iteratively distilled from the notion that "the similarity between two objects is reinforced by the similarity of their related objects". The existing SimFusion model usually exploits the Unified Relationship Matrix (URM) to represent latent relationships among heterogeneous data, and adopts an iterative paradigm for SimFusion computation. However, due to the row normalization of URM, the traditional SimFusion model may produce the trivial solution; worse still, the iterative computation of SimFusion may not ensure the global convergence of the solution. This paper studies the revision of this model, providing a full treatment from complexity to algorithms. (1) We propose SimFusion+ based on a notion of the Unified Adjacency Matrix (UAM), a modification of the URM, to prevent the trivial solution and the divergence issue of SimFusion. (2) We show that for any vertex-pair, SimFusion+ can be performed in O(1) time and O(n) space with an O(km)-time precomputation done only once, as opposed to the O(kn3) time and O(n2) space of its traditional counterpart, where n, m, and k denote the number of vertices, edges, and iterations respectively. (3) We also devise an incremental algorithm for further improving the computation of SimFusion+ when networks are dynamically updated, with performance guarantees for similarity estimation. We experimentally verify that these algorithms scale well, and the revised notion of SimFusion is able to converge to a non-trivial solution, and allows us to identify more sensible structure information in large real-world networks.
Privacy-aware image classification and search
Authors: Sergej Zerr, Stefan Siersdorfer, Jonathon S. Hare, Elena Demidova
Venue: SIGIR
Year: 2012

Modern content sharing environments such as Flickr or YouTube contain a large amount of private resources such as photos showing weddings, family holidays, and private parties. These resources can be of a highly sensitive nature, disclosing many details of the users' private sphere. In order to support users in making privacy decisions in the context of image sharing and to provide them with a better overview on privacy related visual content available on the Web, we propose techniques to automatically detect private images, and to enable privacy-oriented image search. To this end, we learn privacy classifiers trained on a large set of manually assessed Flickr photos, combining textual metadata of images with a variety of visual features. We employ the resulting classification models for specifically searching for private photos, and for diversifying query results to provide users with a better coverage of private and public content. Large-scale classification experiments reveal insights into the predictive performance of different visual and textual features, and a user evaluation of query result rankings demonstrates the viability of our approach.
Emotion tagging for comments of online news by meta classification with heterogeneous information sources
Authors: Ying Zhang, Yi Fang, Xiaojun Quan, Lin Dai, Luo Si, Xiaojie Yuan
Venue: SIGIR
Year: 2012

With the rapid growth of online news services, users can actively respond to online news by making comments. Users often express subjective emotions in comments such as sadness, surprise and anger. Such emotions can help understand the preferences and perspectives of individual users, and therefore may facilitate online publishers to provide users with more relevant services. This paper tackles the task of predicting emotions for the comments of online news. To the best of our knowledge, this is the first research work for addressing the task. In particular, this paper proposes a novel Meta classification approach that exploits heterogeneous information sources such as the content of the comments and the emotion tags of news articles generated by users. The experiments on two datasets from online news services demonstrate the effectiveness of the proposed approach.
See-to-retrieve: efficient processing of spatio-visual keyword queries
Authors: Chao Zhang, Lidan Shou, Ke Chen, Gang Chen
Venue: SIGIR
Year: 2012

The wide proliferation of powerful smart phones equipped with multiple sensors, 3D graphical engine, and 3G connection has nurtured the creation of a new spectrum of visual mobile applications. These applications require novel data retrieval techniques which we call What-You-Retrieve-Is-What-You-See (WYRIWYS). However, state-of-the-art spatial retrieval methods are mostly distance-based and thus inapplicable for supporting WYRIWYS. Motivated by this problem, we propose a novel query called spatio-visual keyword (SVK) query, to support retrieving spatial Web objects that are both visually conspicuous and semantically relevant to the user. To capture the visual features of spatial Web objects with extents, we introduce a novel visibility metric which computes object visibility in a cumulative manner. We propose an incremental method called Complete Occlusion-map based Retrieval (COR) to answer SVK queries. This method exploits effective heuristics to prune the search space and construct a data structure called Occlusion-Map. Then the method adopts the best-first strategy to return relevant objects incrementally. Extensive experiments on real and synthetic data sets suggest that our method is effective and efficient when processing SVK queries.
Learning hash codes for efficient content reuse detection
Authors: Qi Zhang, Yan Wu, Zhuoye Ding, Xuanjing Huang
Venue: SIGIR
Year: 2012

Content reuse is extremely common in user generated mediums. Reuse detection serves as be the basis for many applications. However, along with the explosion of Internet and continuously growing uses of user generated mediums, the task becomes more critical and difficult. In this paper, we present a novel efficient and scalable approach to detect content reuse. We propose a new signature generation algorithm, which is based on learned hash functions for words. In order to deal with tens of billions of documents, we implement the detection approach on graphical processing units (GPUs). The experimental comparison in this paper involves studies of efficiency and effectiveness of the proposed approach in different types of document collections, including ClueWeb09, Tweets2011, and so on. Experimental results show that the proposed approach can achieve the same detection rates with state-of-the-art systems while uses significantly less execution time than them (from 400X to 1500X speedup).
Sentiment identification by incorporating syntax, semantics and context information
Authors: Kunpeng Zhang, Yusheng Xie, Yu Cheng, Daniel Honbo, Doug Downey, Ankit Agrawal, Wei-keng Liao, Alok N. Choudhary
Venue: SIGIR
Year: 2012

This paper proposes a method based on conditional random fields to incorporate sentence structure (syntax and semantics) and context information to identify sentiments of sentences within a document. It also proposes and evaluates two different active learning strategies for labeling sentiment data. The experiments with the proposed approach demonstrate a 5-15% improvement in accuracy on Amazon customer reviews compared to existing supervised learning and rule-based methods.
Rewarding term location information to enhance probabilistic information retrieval
Authors: Jiashu Zhao, Jimmy Xiangji Huang, Shicheng Wu
Venue: SIGIR
Year: 2012

We investigate the effect of rewarding terms according to their locations in documents for probabilistic information retrieval. The intuition behind our approach is that a large amount of authors would summarize their ideas in some particular parts of documents. In this paper, we focus on the beginning part of documents. Several shape functions are defined to simulate the influence of term location information. We propose a Reward Term Retrieval model that combines the reward terms' information with BM25 to enhance probabilistic information retrieval performance.
Evaluating aggregated search pages
Authors: Ke Zhou, Ronan Cummins, Mounia Lalmas, Joemon M. Jose
Venue: SIGIR
Year: 2012

Aggregating search results from a variety of heterogeneous sources or verticals such as news, image and video into a single interface is a popular paradigm in web search. Although various approaches exist for selecting relevant verticals or optimising the aggregated search result page, evaluating the quality of an aggregated page is an open question.
Is uncertain logical-matching equivalent to conditional probability?
Authors: Karam Abdulahhad, Jean-Pierre Chevallet, Catherine Berrut
Venue: SIGIR
Year: 2013

Logic-based Information Retrieval (IR) models represent the retrieval decision as a logical implication d->q between a document d and a query q, where d and q are logical sentences. However, d->q is a binary decision, we thus need a measure to estimate the degree to which d implies q, denoted P(d->q). In this study, we revisit the Van Rijsbergen's assumptions about: 1- the logical implication ->' is not the material one, and 2- P(d->q) could be estimated by the conditional probability P(q|d). More precisely, we claim that the material implication is an appropriate implication for IR, and also we mathematically prove that replacing P(d->q) by P(q|d) is a correct choice. In order to prove the Van Rijsbergen's assumption, we use the Propositional Logic and the Lattice theory. We also exploit the notion of degree of implication that is proposed by Knuth.
Cache-conscious performance optimization for similarity search
Authors: Maha Alabduljalil, Xun Tang, Tao Yang
Venue: SIGIR
Year: 2013

All-pairs similarity search can be implemented in two stages. The first stage is to partition the data and group potentially similar vectors. The second stage is to run a set of tasks where each task compares a partition of vectors with other candidate partitions. Because of data sparsity, accessing feature vectors in memory for runtime comparison in the second stage, incurs significant overhead due to the presence of memory hierarchy. This paper proposes a cache-conscious data layout and traversal optimization to reduce the execution time through size-controlled data splitting and vector coalescing. It also provides an analysis to guide the optimal choice for the parameter setting. Our evaluation with several application datasets verifies the performance gains obtained by the optimization and shows that the proposed scheme is upto 2.74x as fast as the cache-oblivious baseline.
Boosting novelty for biomedical information retrieval through probabilistic latent semantic analysis
Authors: Xiangdong An, Jimmy Xiangji Huang
Venue: SIGIR
Year: 2013

In information retrieval, we are interested in the information that is not only relevant but also novel. In this paper, we study how to boost novelty for biomedical information retrieval through probabilistic latent semantic analysis. We conduct the study based on TREC Genomics Track data. In TREC Genomics Track, each topic is considered to have an arbitrary number of aspects, and the novelty of a piece of information retrieved, called a passage, is assessed based on the amount of new aspects it contains. In particular, the aspect performance of a ranked list is rewarded by the number of new aspects reached at each rank and penalized by the amount of irrelevant passages that are rated higher than the novel ones. Therefore, to improve aspect performance, we should reach as many aspects as possible and as early as possible. In this paper, we make a preliminary study on how probabilistic latent semantic analysis can help capture different aspects of a ranked list, and improve its performance by re-ranking. Experiments indicate that the proposed approach can greatly improve the aspect-level performance over baseline algorithm Okapi BM25.
Document identifier reassignment and run-length-compressed inverted indexes for improved search performance
Authors: Diego Arroyuelo, Senén González, Mauricio Oyarzún, Victor Sepulveda
Venue: SIGIR
Year: 2013

Text search engines are a fundamental tool nowadays. Their efficiency relies on a popular and simple data structure: the inverted indexes. Currently, inverted indexes can be represented very efficiently using index compression schemes. Recent investigations also study how an optimized document ordering can be used to assign document identifiers (docIDs) to the document database. This yields important improvements in index compression and query processing time. In this paper we follow this line of research, yet from a different perspective. We propose a docID reassignment method that allows one to focus on a given subset of inverted lists to improve their performance. We then use run-length encoding to compress these lists (as many consecutive 1s are generated). We show that by using this approach, not only the performance of the particular subset of inverted lists is improved, but also that of the whole inverted index. Our experimental results indicate a reduction of about 10% in the space usage of the whole index docID reassignment was focused. Also, decompression speed is up to 1.22 times faster if the runs must be explicitly decompressed and up to 4.58 times faster if implicit decompression of runs is allowed. Finally, we also improve the Document-at-a-Time query processing time of AND queries (by up to 12%), WAND queries (by up to 23%) and full (non-ranked) OR queries (by up to 86%).
Competition-based networks for expert finding
Authors: Çigdem Aslay, Neil O&apos, Hare, Luca Maria Aiello, Alejandro Jaimes
Venue: SIGIR
Year: 2013

Finding experts in question answering platforms has important applications, such as question routing or identification of best answers. Addressing the problem of ranking users with respect to their expertise, we propose Competition-Based Expertise Networks (CBEN), a novel community expertise network structure based on the principle of competition among the answerers of a question. We evaluate our approach on a very large dataset from Yahoo! Answers using a variety of centrality measures. We show that it outperforms state-of-the-art network structures and, unlike previous methods, is able to consistly outperform simple metrics like best answer count. We also analyse question answering forums in Yahoo! Answers, and show that they can be characterised by factual or subjective information seeking behavior, social discussions and the conducting of polls or surveys. We find that the ability to identify experts greatly depends on the type of forum, which is directly reflected in the structural properties of the expertise networks.
Cumulative citation recommendation: classification vs. ranking
Authors: Krisztian Balog, Heri Ramampiaro
Venue: SIGIR
Year: 2013

Cumulative citation recommendation refers to the task of filtering a time-ordered corpus for documents that are highly relevant to a predefined set of entities. This task has been introduced at the TREC Knowledge Base Acceleration track in 2012, where two main families of approaches emerged: classification and ranking. In this paper we perform an experimental comparison of these two strategies using supervised learning with a rich feature set. Our main finding is that ranking outperforms classification on all evaluation settings and metrics. Our analysis also reveals that a ranking-based approach has more potential for future improvements.
A document rating system for preference judgements
Authors: Maryam Bashir, Jesse Anderton, Jie Wu, Peter B. Golbus, Virgil Pavlu, Javed A. Aslam
Venue: SIGIR
Year: 2013

High quality relevance judgments are essential for the evaluation of information retrieval systems. Traditional methods of collecting relevance judgments are based on collecting binary or graded nominal judgments, but such judgments are limited by factors such as inter-assessor disagreement and the arbitrariness of grades. Previous research has shown that it is easier for assessors to make pairwise preference judgments. However, unless the preferences collected are largely transitive, it is not clear how to combine them in order to obtain document relevance scores. Another difficulty is that the number of pairs that need to be assessed is quadratic in the number of documents. In this work, we consider the problem of inferring document relevance scores from pairwise preference judgments by analogy to tournaments using the Elo rating system. We show how to combine a linear number of pairwise preference judgments from multiple assessors to compute relevance scores for every document.
Beyond relevance: on novelty and diversity in tag recommendation
Authors: Fabiano Belém
Venue: SIGIR
Year: 2013

We propose to explicitly exploit issues related to novelty and diversity in tag recommendation tasks, an unexplored research avenue (only relevance issues have been investigated so far), in order to improve user experience and satisfaction. We propose new tag recommendation strategies to cover these issues and highlight the involved challenges.
Pseudo test collections for training and tuning microblog rankers
Authors: Richard Berendsen, Manos Tsagkias, Wouter Weerkamp, Maarten de Rijke
Venue: SIGIR
Year: 2013

Recent years have witnessed a persistent interest in generating pseudo test collections, both for training and evaluation purposes. We describe a method for generating queries and relevance judgments for microblog search in an unsupervised way. Our starting point is this intuition: tweets with a hashtag are relevant to the topic covered by the hashtag and hence to a suitable query derived from the hashtag. Our baseline method selects all commonly used hashtags, and all associated tweets as relevance judgments; we then generate a query from these tweets. Next, we generate a timestamp for each query, allowing us to use temporal information in the training process. We then enrich the generation process with knowledge derived from an editorial test collection for microblog search.
Searching in the city of knowledge: challenges and recent developments
Authors: Veli Bicer, Vanessa López
Venue: SIGIR
Year: 2013

Today plenty of data is emerging from various city systems. Beyond the classical Web resources, large amounts of data are retrieved from sensors, devices, social networks, governmental applications, or service networks. In such a diversity of information, answering specific information needs of city inhabitants requires holistic IR techniques, capable of harnessing different types of city data and turned it into actionable insights to answer different queries. This tutorial will present deep insights, challenges, opportunities and techniques to make heterogeneous city data searchable and show how emerging IR techniques models can be employed to retrieve relevant information for the citizens.
A geolinguistic web application based on linked open data
Authors: Emanuele Di Buccio, Giorgio Maria Di Nunzio, Gianmaria Silvello
Venue: SIGIR
Year: 2013

Digital Geolinguistic systems encourage collaboration between linguists, historians, archaeologists, ethnographers, as they explore the relationship between language and cultural adaptation and change. In this demo, we propose a Linked Open Data approach for increasing the level of interoperability of geolinguistic applications and the reuse of the data. We present a case study of a geolinguistic project named Atlante Sintattico d'Italia, Syntactic Atlas of Italy (ASIt).
Scalability and efficiency challenges in commercial web search engines
Authors: Berkant Barla Cambazoglu, Ricardo Baeza-Yates
Venue: SIGIR
Year: 2013

Commercial web search engines rely on very large compute infrastructures to be able to cope with the continuous growth of the Web and user bases. Achieving scalability and efficiency in such large-scale search engines requires making careful architectural design choices while devising algorithmic performance optimizations. Unfortunately, most details about the internal functioning of commercial web search engines remain undisclosed due to their financial value and the high level of competition in the search market. The main objective of this tutorial is to provide an overview of the fundamental scalability and efficiency challenges in commercial web search engines, bridging the existing gap between the industry and academia.
Workshop on benchmarking adaptive retrieval and recommender systems: BARS 2013
Authors: Pablo Castells, Frank Hopfgartner, Alan Said, Mounia Lalmas
Venue: SIGIR
Year: 2013

Evaluating adaptive and personalized information retrieval tech-niques is known to be a difficult endeavor. The rapid evolution of novel technologies in this scope raises additional challenges that further stress the need for new evaluation approaches and method-ologies. The BARS 2013 workshop seeks to provide a specific venue for work on novel, personalization-centric benchmarking approaches to evaluate adaptive retrieval and recommender systems.