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Cross-Lingual Topic Discovery From Multilingual Search Engine Query Log

Today, major commercial search engines are operating in a multinational fashion to provide web search services for millions of users who compose... (more)

Ranking-Oriented Collaborative Filtering

Collaborative filtering (CF) is one of the most effective techniques in recommender systems, which can be either rating oriented or ranking oriented. Ranking-oriented CF algorithms demonstrated significant performance gains in terms of ranking accuracy, being able to estimate a precise preference ranking of items for each user rather than the... (more)

Joint Modeling of User Check-in Behaviors for Real-time Point-of-Interest Recommendation

Point-of-Interest (POI) recommendation has become an important means to help people discover attractive and interesting places, especially when users... (more)

Boosting Recommendation in Unexplored Categories by User Price Preference

State-of-the-art methods for product recommendation encounter a significant performance drop in categories where a user has no purchase history. This... (more)

Context Trees

Exposing latent knowledge in geospatial trajectories has the potential to provide a better understanding of the movements of individuals and groups. Motivated by such a desire, this work presents the context tree, a new hierarchical data structure that summarises the context behind user actions in a single model. We propose a method for context... (more)

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Call for Papers

ACM Transactions on Information Systems (TOIS) Call for submissions to a Special Issue on Search, Mining and their Applications on Mobile Devices. Click here to see more details.

Call for Special Issue Proposals

ACM Transactions on Information Systems (TOIS) invites proposals for a special issue of the journal devoted to any topic in information retrieval. Click here to see more details.

New options for ACM authors to manage rights and permissions for their work

 

ACM introduces a new publishing license agreement, an updated copyright transfer agreement, and a new authorpays option which allows for perpetual open access through the ACM Digital Library. For more information, visit the ACM Author Rights webpage.

 

About TOIS

ACM Transactions on Information Systems (TOIS) is a scholarly journal that publishes previously unpublished high-quality scholarly articles in all areas of information retrieval. TOIS is published quarterly.

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Forthcoming Articles
Re-finding Behaviour in Vertical Domains

We differentiate and model user re-finding behavior within different media and topic domains that are related to the domains of vertical search engines (e.g. images, news, reference material, and movies). We distinguish the re-finding behavior from general search, and engineer features that are effective in differentiating re-finding across the domains. The features are then used to build machine learned models. While the accuracy of detection is 85.7%, compared with 97.5% when examining re-finding from general search tasks, to the best of our knowledge such an examination of vertical re-finding has not been tried before. We attempt to differentiate re-finding behavior when the history of a searchers interactions are not available. In this scenario we achieve an average accuracy of 77.5% across the domains. We also examine early detection of re-finding during a searchers session. Finally, we investigate in which types of domains is re-finding most difficult. Here, it would appear that re-finding images is particularly challenging for users. This research has implications for search engine design, in terms of adapting search results by predicting the type of user tasks and enabling the presentation of vertical-specific results when re-finding is identified.

Hilbert Exclusion: Improved Metric Search through Finite Isometric Embeddings

Most research into similarity search in metric spaces relies upon the triangle inequality property. This property allows the space to be arranged according to relative distances to avoid searching some subspaces. We show that many common metric spaces, notably including those using Euclidean and Jensen-Shannon distances, also have a stronger property, sometimes called the four-point property: in essence, these spaces allow an isometric embedding of any four points in three-dimensional Euclidean space, as well as any three points in two-dimensional Euclidean space. In fact, we show that any space which is isometrically embeddable in Hilbert space has the stronger property. This property gives stronger geometric guarantees, and one in particular, which we name the Hilbert Exclusion property, allows any indexing mechanism which uses hyperplane partitioning to perform better. One outcome of this observation is that a number of state-of-the-art indexing mechanisms over high dimensional spaces can be easily extended to give a significant increase in performance; furthermore, the improvement given is greater in higher dimensions. This therefore leads to a significant improvement in the cost of metric search in these spaces.

Inverted Treaps

We introduce a new representation of the inverted index that performs faster ranked unions and intersections while using less space. Our index is based on the treap data structure, which allows us to intersect/merge the document identifiers while simultaneously thresholding by frequency, instead of the costlier two-step classical processing methods. To achieve compression we represent the treap topology using different alternative compact data structures. Further, the treap invariants allow us to elegantly encode differentially both document identifiers and frequencies. We also show how to extend this representation to support incremental updates over the index. Results show that, under the tf-idf scoring scheme, our index uses about the same space as state-of-the-art compact representations, while performing up to 220 times faster on ranked single-word, union, or intersection queries. Under the BM25 scoring scheme, our index may use up to 40% more space than the others and outperforms them less frequently, but still reaches improvement factors of 220 in the best cases. The index supporting incremental updates poses an overhead of 50%100% over the static variants in terms of space, construction and query time.

Cost-effective Online Trending Topic Detection and Popularity Prediction in Microblogging

Identifying topic trends on microblogging services such as Twitter and estimating those topics' future popularity have great academic and business value, especially when the operations can be done in real time. For any third party, however, retrieving and processing such huge volumes of real-time data in microblogs are almost infeasible tasks, as there always exist API request limits, monitoring and computing budgets, as well as timeliness requirements. To deal with these challenges, we propose a cost-effective system framework with algorithms that can automatically select an optimal subset of users in microblogging networks, under cost constraints. Then the proposed system can monitor and utilize only these selected users' real-time microposts to detect the overall trending topics and predict their future popularity among the whole microblogging network. Therefore, our proposed system framework is practical for real-time usage as it avoids retrieving and processing full real-time data, while not compromising detection and prediction performance under given cost constraints. Experiments with real microblogs dataset show that by tracking only 800 users out of 0.7 million users and processing no more than 30,000 microposts daily, about 75% trending topics could be detected and predicted by the proposed system, and on average 4.6 hours earlier than they appear in official trends list.

Derive User Preferences of Mobile Apps from their Management Activities

Smartphone users adopt explosive number of mobile applications (a.k.a., apps) in the recent years. App marketplaces for iOS, Android and Windows Phone platforms host millions of apps with billions of downloads. Investigating how people manage mobile apps in their everyday lives creates a unique opportunity to understand the behavior and preferences of mobile device users, to infer the quality of apps, and to improve user experience. Existing literature provides very limited knowledge about app management activities, due to the lack of app usage data at scale. This paper takes the initiative to analyze a very large app management log collected through a leading Android app marketplace. The data set covers five months of detailed downloading, updating, and uninstallation activities, which involve 17 million anonymized users and one million apps. We present a surprising finding that the metrics commonly used to rank apps in app stores do not truly reflect the users' real attitudes. We then identify behavioral patterns from the app management activities that more accurately indicate user preferences of an app even when no explicit rating is available. A systematic statistical analysis is designed to evaluate machine learning models that predict user preferences using these behavioral patterns.

Cross-Platform App Recommendation by Jointly Modeling Ratings and Texts

Over the last decade, the renaissance of Web technologies has transformed the online world into an application (App) driven society. While the abundant Apps have provided great convenience, their sheer number also leads to severe information overload, making it difficult for users to identify desired Apps. To alleviate the information overloading issue, recommender systems have been proposed and deployed for the online App domain. However, existing work on App recommendation has largely focused on one single platform, while ignores the rich and relevant data of other platforms. In this paper, we tackle the problem of cross-platform App recommendation, aiming at leveraging users' and Apps' data on multiple platforms to enhance the recommendation accuracy that bases on one single platform. One key advantage of our proposal is that by leveraging the multi-platform data, the perpetual issues in personalized recommender systems, data sparsity and cold-start, can be largely alleviated. To this end, we propose a sound and principled solution that integrates both the numerical ratings and textual content extracted from multiple platforms. Our solution innovatively represents an App as an aggregation of common features across platforms and specific features on the resided platform. Extensive experiments on real-world datasets have demonstrated the promising performance of our proposed approach.

Fast Ranking with Additive Ensembles of Oblivious and Non-Oblivious Regression Trees

Learning-to-Rank models based on additive ensembles of regression trees have been proven to be very effective for scoring query results returned by large-scale Web search engines. Unfortunately, the computational cost of scoring thousands of candidate documents by traversing large ensembles of trees is high. Thus, several works have investigated solutions aimed at improving the efficiency of document scoring by exploiting advanced features of modern CPUs and memory hierarchies. In this paper, we present QuickScorer, a new algorithm that adopts a novel cache-efficient representation of a given tree ensemble, it performs an interleaved traversal by means of fast bitwise operations, and also supports ensembles of oblivious trees. An extensive and detailed test assessment is conducted on two standard learning-to-rank datasets and on a novel very-large dataset we made publicly available for conducting significant efficiency tests. The experiments show unprecedented speedups over the best state-of-the-art baselines ranging from 1.9x to 6.6x. The analysis of low-level profiling traces shows that QuickScorer efficiency is due to its cache-aware approach both in terms of data layout and access patterns, and to a control flow that entails very low branch mis-prediction rates.

Time-Aware Click Model

Click-through information is considered as a valuable source of users implicit relevance feedback for commercial search engines. As existing studies have shown that search result position in search engine result page(SERP) has a very strong influence on users examination behavior, most existing click models are position-based, assuming that users examine results from top to bottom in a linear fashion. While these click models have been successful, most do not take temporal information into account. As many existing studies have shown, click dwell time and click sequence information are strongly correlated with users perceived relevance and search satisfaction. In this paper, we investigate the problem of properly incorporating temporal information into click models. We firstly carry out a laboratory eye-tracking study to analyze users examination behavior in different click sequences. Afterwards, we analyze dwell time distribution in different search logs and find that we cannot simply use a click dwell time threshold to distinguish relevant/irrelevant results. Finally, we propose a novel click model named Time-Aware Click Model (TACM) that captures the temporal information of user behavior. Experimental results on practical search logs show that TACM outperforms other models in terms of both predicting click behavior (perplexity) and estimating result relevance (NDCG).

Learning Informative Priors from Heterogeneous Domains to Improve Recommendation in Cold-Start User Domains

Users often have sufficient experience in their focused domains but lack experience in others. Recommender systems are helpful for recommending potentially desirable items to users in unfamiliar domains, and cross-domain collaborative filtering is therefore an important emerging research topic. However, it is inevitable that the cold-start issue will be encountered in unfamiliar domains due to the lack of feedback data. The Bayesian approach shows that priors play an important role when there are insufficient data, which implies that recommendation performance can be significantly improved in cold-start domains if informative priors can be provided. Based on this idea, we propose a Weighted Irregular Tensor Factorization (WITF) model to leverage multi-domain feedback data to learn the cross-domain priors w.r.t. both users and items. The features learned from WITF serve as the informative priors on the latent factors of users and items in terms of weighted matrix factorization models. Moreover, WITF is a unified framework for dealing with both explicit feedback and implicit feedback. To prove the effectiveness of our approach, we studied three typical real-world cases in which a collection of empirical evaluations were conducted on real-world datasets to compare the performance of our model and other state-of-the-art approaches. The results show the superiority of our model over comparable models.

Version-Aware Rating Prediction for Mobile App Recommendation

With the great popularity of mobile devices, the amount of mobile apps has grown in a dramatic rate than we have ever expected. A technical challenge is how to recommend suitable apps to mobile users. In this work, we identify and focus on a unique characteristic that exists in mobile app recommendation, i.e., an app usually corresponds to multiple release versions. Based on this characteristic, we propose a fine-grained version-aware app recommendation problem. Instead of directly learning the users' preferences over the apps, we aim to infer the ratings of users on a specific version of an app. However, the user-version rating matrix will be sparser than the corresponding user-app rating matrix, making existing recommendation methods less effective. In view of this, our approach has made two major extensions. First, we leverage the review text that is associated with each rating record; more importantly, we consider two types of version-based correlations. The first type is to capture the temporal correlations between multiple versions within the same app, and the second type of correlation is to capture the aggregation correlations between similar apps. Experimental results on a large data set demonstrate the superiority of our approach over several competitive methods.

Search Result Prefetching on Desktop and Mobile

Search result examination is an important part of searching. High page load latency for landing pages (clicked search results) can reduce the efficiency of the search process. Proactively prefetching landing pages in advance of clickthrough can save searchers valuable time. However, prefetching consumes resources (primarily bandwidth and battery) that are wasted unless the prefetched results are requested by searchers. Balancing the costs in prefetching particular results against the benefits in reduced latency to searchers represents the search result prefetching challenge. In this article we introduce this challenge and present methods to address it in both desktop and mobile settings. Our methods leverage searchers' cursor movements (on desktop) and viewport-based viewing behavior (on mobile) on search engine result pages (SERPs) in real time to dynamically estimate the result that searchers will request next. We demonstrate through large-scale log analysis that our approach significantly outperforms three strong baselines that prefetch results based on (i) the search engine result ranking (prefetch top-ranked results), (ii) past SERP clicks from all searchers for the query (prefetch popular results), or (iii) past SERP clicks from the current searcher for the query (prefetch results that the searcher prefers). Our methods can make the search process more efficient.

Targeted Advertising in Public Transportation Systems with Quantitative Evaluation

In spite of vast business potential, targeted advertising in public transportation systems is a grossly unexplored research area. For instance, SBS Transit in Singapore can reach 1 billion passengers per year but the annual advertising revenue contributes less than $35 million. To bridge the gap, we propose a probabilistic data model that captures the motion patterns and user interests so as to quantitatively evaluate the impact of an advertisement among the passengers. In particular, we leverage hundreds of millions of bus/train boarding transaction records to quantitatively estimate the probability as well as the extent of a user being influenced by an ad. Based on the influence model, we study a top-k retrieval problem for bus/train ad recommendation, which acts as a primitive operator to support various advanced applications. We solve the retrieval problem efficiently to support real-time decision making. In the experimental study, we use the dataset from SBS Transit as a case study to verify the effectiveness and efficiency of our proposed methodologies. Furthermore, we implement an insightful interface to visualize the advertising impact.

On Crowdsourcing Relevance Magnitudes for Information Retrieval Evaluation

Magnitude estimation is a psychophysical scaling technique for the measurement of sensation, where observers assign numbers to stimuli in response to their perceived intensity. We investigate the use of magnitude estimation for judging the relevance of documents for information retrieval evaluation, carrying out a large-scale user study across 18 TREC topics and collecting over 50,000 magnitude estimation judgments using crowd sourcing. Our analysis shows that, on average, magnitude estimation judgments are rank-aligned with ordinal judgments made by expert relevance assessors. We explore the application of magnitude estimation for IR evaluation, calibrating two gain-based effectiveness metrics, nDCG and ERR, directly from user-reported perceptions of relevance. A comparison of TREC system effectiveness rankings based on binary, ordinal, and magnitude estimation relevance shows substantial variation; in particular, the top systems ranked using magnitude estimation and ordinal judgments differ substantially. Analysis of the magnitude estimation scores shows that this effect is due in part to varying perceptions of relevance: different users have different perceptions of the impact of relative differences in document relevance. These results have direct implications for IR evaluation, suggesting that current assumptions about a single view of relevance being sufficient to represent a population of users are unlikely to hold.

Bibliometrics

Publication Years 1983-2016
Publication Count 645
Citation Count 20086
Available for Download 645
Downloads (6 weeks) 6074
Downloads (12 Months) 35762
Downloads (cumulative) 732441
Average downloads per article 1136
Average citations per article 31
First Name Last Name Award
Hal Berghel Outstanding Contribution to ACM Award (2000)
ACM Distinguished Service Award (1996)
Daniel Bobrow ACM Software System Award (1992)
ACM Programming Systems and Languages Paper Award (1974)
Kellogg S. Booth ACM Distinguished Member (2009)
ACM Senior Member (2009)
David Carmel ACM Distinguished Member (2016)
Carlos A. Castillo ACM Senior Member (2014)
Charles L A Clarke ACM Distinguished Member (2015)
Fabio Crestani ACM Senior Member (2009)
Ernesto Damiani ACM Distinguished Member (2008)
Susan T Dumais ACM Athena Lecturer Award (2014)
Ahmed Elmagarmid ACM Distinguished Member (2009)
Deborah Estrin ACM Athena Lecturer Award (2006)
Elena Ferrari ACM Distinguished Member (2011)
Alvaro Barreiro Garcia ACM Senior Member (2009)
Calvin C. Gotlieb Outstanding Contribution to ACM Award (2011)
ACM Presidential Award (2002)
Wendy Hall Outstanding Contribution to ACM Award (2014)
Jonathan L Herlocker ACM Software System Award (2010)
Djoerd Hiemstra ACM Senior Member (2009)
Eric Horvitz ACM AAAI Allen Newell Award (2015)
Xian-Sheng Hua ACM Distinguished Member (2015)
ACM Senior Member (2009)
Tiejun Huang ACM Senior Member (2013)
Xiangji Huang ACM Senior Member (2016)
Min-Yen Kan ACM Senior Member (2009)
David Karger ACM Doctoral Dissertation Award (1994)
Won Kim ACM Distinguished Service Award (2001)
Jon Kleinberg ACM AAAI Allen Newell Award (2014)
ACM Prize in Computing (2008)
Joseph A Konstan ACM Software System Award (2010)
ACM Distinguished Member (2006)
Wessel Kraaij ACM Senior Member (2007)
Raymond A Lorie ACM Software System Award (1988)
ACM Programming Systems and Languages Paper Award (1982)
David E. Losada ACM Senior Member (2011)
Robert WP Luk ACM Senior Member (2007)
Yoelle Maarek ACM Distinguished Member (2010)
Gary Marchionini ACM Senior Member (2008)
Satoshi Matsuoka ACM Gordon Bell Prize
Tokyo Institute of Technology (2011) ACM Gordon Bell Prize
Tokyo Institute of Technology (2011)
Kathryn Mckinley ACM Distinguished Member (2006)
ACM Senior Member (2006)
Tao Mei ACM Distinguished Member (2016)
ACM Senior Member (2012)
Stuart Edward Middleton ACM Senior Member (2014)
George Andrei Mihaila ACM Senior Member (2008)
Bradley N Miller ACM Software System Award (2010)
Mario A Nascimento ACM Senior Member (2007)
Hwee Tou Ng ACM Distinguished Member (2010)
Dianne P O'Leary ACM Distinguished Member (2006)
Judith S Olson ACM Athena Lecturer Award (2011)
Randy Pausch ACM Karl V. Karlstrom Outstanding Educator Award (2007)
David M Pennock ACM Senior Member (2006)
Vijay V Raghavan ACM Distinguished Member (2012)
Paul J Resnick ACM Software System Award (2010)
John T Riedl ACM Software System Award (2010)
ACM Distinguished Member (2007)
Mehran Sahami ACM Presidential Award (2014)
Fabio A. Schreiber ACM Senior Member (2012)
Robert P. Schumaker ACM Senior Member (2012)
Frank Shipman ACM Distinguished Member (2009)
Michael Stonebraker ACM A. M. Turing Award (2014)
ACM Software System Award (1988)
Jaime Teevan ACM Senior Member (2012)
Loren Terveen ACM Distinguished Member (2009)
Jennifer Widom ACM Athena Lecturer Award (2015)
Hui Xiong ACM Distinguished Member (2014)
ACM Senior Member (2010)
Qiang Yang ACM Distinguished Member (2011)
Shumin Zhai ACM Distinguished Member (2006)
Zhi-Hua Zhou ACM Distinguished Member (2013)
ACM Senior Member (2011)
Zhi-Hua Zhou ACM Distinguished Member (2013)
ACM Senior Member (2011)

First Name Last Name Paper Counts
Tatseng Chua 8
Justin Zobel 8
William Croft 8
Hsinchun Chen 8
Norbert Fuhr 7
Bin Cui 7
Stavros Christodoulakis 6
Hector Garcia-Molina 6
Gonzalo Navarro 5
Kalervo Järvelin, 5
Oren Kurland 5
Dennis Tsichritzis 5
Alistair Moffat 5
John Gould 5
Zi Huang 5
Thomas, Malone 5
Maarten De Rijke 5
Roger King 5
John Leggett 4
Ophir Frieder 4
Dennis McLeod 4
Prasenjit Mitra 4
Hugh Williams 4
Susan Dumais 4
Ricardo Baeza-Yates 4
Ahmed Abbasi 4
Fabio Crestani 4
ChengXiang Zhai 4
Kamfai Wong 4
Jimmy Lin 4
Hengtao Shen 4
Swapan Parui 3
Mark Sanderson 3
Dawei Song 3
Luis Gravano 3
Fausto Rabitti 3
Elisa Bertino 3
Abdur Chowdhury 3
Joseph Konstan 3
Liqiang Nie 3
Alexander Tuzhilin 3
Amihai Motro 3
Kumyew Lai 3
Heikki Keskustalo 3
Hongzhi Yin 3
Luo Si 3
Nieves Brisaboa 3
Matthias Jarke 3
Christos Faloutsos 3
Henry Gladney 3
Raffaele Perego 3
Fabrizio Silvestri 3
Ge Nong 3
Xiaofang Zhou 3
Roy Rada 3
Robert Luk 3
Kuilam Kwok 3
Marjan Celikik 3
Nívio Ziviani 3
Eric Horvitz 3
Abraham Bookstein 3
David Carmel 3
Simon Gibbs 3
William Cohen 3
Pavel Zezula 3
Edleno De Moura 3
Cornelis Van Rijsbergen 3
Shmuel Klein 3
Jamie Callan 3
Wai Lam 3
Ben Shneiderman 3
Gary Marchionini 3
Ryen White 3
Hannah Bast 3
Edward Dang 2
Jianyun Nie 2
Claudio Carpineto 2
Chirag Shah 2
Shane Culpepper 2
Waihong Chan 2
Nat Ballou 2
Shipeng Li 2
Hui Yang 2
Gautam Pant 2
Hui Xiong 2
Jonathan Grudin 2
Benjamin Piwowarski 2
Filip Radlinski 2
Craig Macdonald 2
Lillian Lee 2
Ian Ruthven 2
Stefan Siersdorfer 2
John Riedl 2
Frank Tompa 2
Özgür Ulusoy 2
Frederick Lochovsky 2
Darrell Woelk 2
Ronny Lempel 2
Zhiting Hu 2
Robert Krovetz 2
Raymond Panko 2
Charles Wiecha 2
Fazli Can 2
Warren Greiff 2
Seungwon Hwang 2
Vijay Raghavan 2
Mark Chignell 2
Alan Hevner 2
Rong Jin 2
Eric Jensen 2
Panagiotis Ipeirotis 2
Sakti Pramanik 2
Jiaul Paik 2
Mandar Mitra 2
Piero Fraternali 2
Paolo Paolini 2
Berthier Ribeiro-Neto 2
Ling Feng 2
Brian Cooper 2
Sanda Harabagiu 2
Daniel Zeng 2
Mark Carman 2
David Hawking 2
Carson Woo 2
Thorsten Joachims 2
Stuart Middleton 2
Jirong Wen 2
David Hicks 2
Michael Lyu 2
Jialie Shen 2
Howard Turtle 2
Qiang Zhu 2
Wanda Orlikowski 2
Kotagiri Ramamohanarao 2
Iadh Ounis 2
William Webber 2
Hong Cheng 2
Fernando Diaz 2
Anna Shtok 2
Barbara Pernici 2
Carmel Domshlak 2
Leefeng Chien 2
Clyde Giles 2
Sudha Ram 2
Padmini Srinivasan 2
Weiyi Meng 2
Jaime Teevan 2
Raymond Lau 2
Laurence Park 2
Keke Chen 2
Fred Damerau 2
Nick Craswell 2
Nigel Shadbolt 2
Xiangji Huang 2
Lucy Suchman 2
Antonio Siochi 2
Steven Beitzel 2
Waynexin Zhao 2
Tieyan Liu 2
Elena Baralis 2
Paolo Garza 2
Scott Hudson 2
Joemon Jose 2
Christopher Higgins 2
Mansur Kabuka 2
Nicole Yankelovich 2
Antonio Fariña, 2
Zhaohui Zheng 2
Gediminas Adomavicius 2
Jinxi Xu 2
Shibing Yao 2
Clayton Lewis 2
Xiaojun Wan 2
William Jones 2
Peter Bruza 2
Mounia Lalmas 2
Gordon Cormack 2
Jaana Kekäläinen 2
Arjen De Vries 2
David Vallet 2
Yoelle Maarek 2
Dou Shen 2
John Mylopoulos 2
Paul Stotts 2
Qiang Yang 2
George Fitzmaurice 2
Anthony Kaye 2
Olivia Sheng 2
John Schnase 2
Douglas Terry 2
Jennifer Widom 2
Ismail Altingövde 2
Ronan Cummins 2
Maria Fugini 2
Essam El-Kwae 2
John Carroll 2
Hochung Wu 2
Loren Terveen 2
Tao Mei 2
Javed Mostafa 2
Quan Yuan 2
Gao Cong 2
Pasquale Savino 2
Hady Lauw 2
Eepeng Lim 2
Hongyan Liu 2
Richard Furuta 2
Jiantao Sun 2
Stephen Boies 2
Hideki Koike 2
Robert Capra 2
Massimo Melucci 2
Salvatore Orlando 2
Susan Gauch 2
Edward Fox 2
Pável Calado 2
Tao Tao 2
Irwin King 2
Neil Rowe 2
Yong Yu 2
Manolis Koubarakis 2
Randall Trigg 2
Letizia Tanca 2
Marcel Reinders 2
Giovanni Romano 2
Dimitris Papadias 2
Jorge Garza 2
Won Kim 2
Jiashu Zhao 2
Stanley Zdonik 2
Seungtaek Park 1
Wop Nawijn 1
Dirk Bahle 1
Jiawei Han 1
Lei Zhu 1
AlBing Rao 1
Eric Nyberg 1
Bernard Tan 1
Gary Olson 1
Judith Olson 1
Hiroshi Ishii 1
Minoru Kobayashi 1
Xiaobing Xue 1
G Chen 1
Chris Schmandt 1
Vesper Owei 1
S Suthasinekul 1
Jian Su 1
Padhraic Smyth 1
Teekiah Chia 1
Junyu Xuan 1
Noel O'Connor 1
Shaowei Liu 1
Wenwu Zhu 1
Tung Bui 1
Tim Bray 1
Augusto Celentano 1
Sushil Jajodia 1
Brigitte Rock 1
Georg Gottlob 1
Jon Gulla 1
Loc Do 1
Anni Järvelin 1
Artur Pessoa 1
Shay Kutten 1
Michael Siegel 1
Radford Neal 1
Marcin Kaszkiel 1
Mathew Palakal 1
Javier Tejedor 1
Igor Szöke 1
Djoerd Hiemstra 1
Lidong Bing 1
Anagha Kulkarni 1
Jay Israel 1
Ronald Rice 1
Meng Wang 1
Xudong Zhang 1
Dongdong Shan 1
Aditya Pal 1
Zhiyong Cheng 1
Xuemeng Song 1
Qing Li 1
James Allan 1
Zhen Yue 1
Gang Wang 1
Susanna Barber 1
Jay Ponte 1
Yi Chang 1
Fatemeh Zahedi 1
Jerry Farrell 1
Paul Thistlewaite 1
N Vujovic 1
Dragana Brzakovic 1
John Tang 1
Stuart Card 1
Andreas Lemke 1
Gerhard Fischer 1
John Wilbur 1
Philip Yu 1
Oren Etzioni 1
Mark Green 1
Clement Yu 1
Minyen Kan 1
Helena Webb 1
Omer Rana 1
Md Alim 1
My Thai 1
Hien Nguyen 1
Sana Hamdi 1
Amel Bouzeghoub 1
Wassim El-Hajj 1
Khaled Shaban 1
Luca Cagliero 1
Hongwei Liang 1
Norman Delisle 1
Qifan Wang 1
Stan Lanning 1
ShiHuo Chang 1
Yuexian Hou 1
Jie Shao 1
Jongwook Kim 1
Qing Li 1
Milad Shokouhi 1
Steven Hoi 1
Rafi Nachmias 1
David Elsweiler 1
Mark Baillie 1
Célia Dos Santos 1
Qiang Xue 1
Uffe Wiil 1
John Lafferty 1
Hsijian Lee 1
Philip Wu 1
Jiajun Liu 1
Eileen Trauth 1
Maria Theodoridou 1
David Choy 1
Roberto Zicari 1
Bart Dhoedt 1
Jingdong Wang 1
Zhiyong Wang 1
Dawei Luo 1
Lifeng Jia 1
Jonathan Herlocker 1
Thomas Little 1
Raymond Ng 1
Steve Jones 1
Ioan Tăbuş 1
Siranush Radicati 1
Carolyn Crouch 1
Wolfgang Effelsberg 1
Tsunghan Tsai 1
Charalampos Chelmis 1
Fernando Flores 1
Esa Auramaki 1
Kalle Lyytinen 1
Anatol Holt 1
Rodrick Megraw 1
Francisco Lerch 1
Jeongwoo Ko 1
Teruko Mitamura 1
Hweehwa Pang 1
Xiaoyong Du 1
Kwokkee Wei 1
Wei Chu 1
Long Jiang 1
Diane Strong 1
Jonathan Hull 1
Thomas White 1
Paul Hart 1
Deborah Estrin 1
Tomihisa Kamada 1
Cyrano Cabarrus 1
Matthew Conway 1
Chen Chang 1
Jock Mackinlay 1
George Robertson 1
Kangheng Wu 1
Charu Aggarwal 1
David Zeltzer 1
Yunqi Sun 1
Carole Hafner 1
Geoff Walsham 1
Liming Ren 1
Maria Hanrahan 1
Moshe Zloof 1
Vance Waddle 1
Calvin Gotlieb 1
Dan Moldovan 1
Fabio Salice 1
Tsongli Wang 1
Jennifer Eberhardt 1
Jonathan Cohen 1
Sebastiano Vigna 1
Mary Rosson 1
Kazem Taghva 1
Timo Raita 1
John Guiver 1
Salvatore Garruzzo 1
Michael Stonebraker 1
Vasilis Delis 1
Paulo Masiero 1
Marcelo Turine 1
Diego Puppin 1
Wendy Lehnert 1
Sholom Weiss 1
Woojin Paik 1
Matteo Dell'Amico 1
Licia Capra 1
Frederik Transier 1
Thomas Lynam 1
Peter Sander 1
Andreas Oberweis 1
Sen Zhang 1
Dave Straube 1
Yariv Aridor 1
Nicola Ferro 1
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Universite Pierre et Marie Curie 1
Florida State University 1
Instituto de Engenharia de Sistemas e Computadores Investigacao e Desenvolvimento em Lisboa 1
University of Neuchatel 1
University of Quebec System 1
Bar-Ilan University 1
Hefei University of Technology 1
University of Helsinki 1
University of Haifa 1
University of Victoria 1
Courant Institute of Mathematical Sciences 1
Princeton University 1
Claremont Graduate University 1
Deutsche Telekom 1
Towson University 1
University of Waikato 1
Xuzhou Normal University 1
Chungbuk National University 1
Indian Institute of Technology, Kharagpur 1
University of Arkansas - Fayetteville 1
Instituto Superior Tecnico 1
University College Dublin 1
Korea University 1
Rice University 1
University of Insubria 1
Monterey Bay Aquarium Research Institute 1
Colby College 1
University of Houston 1
University of Pennsylvania 1
MIT Computer Science and Artificial Intelligence Laboratory 1
University of Koblenz-Landau 1
University of Turku 1
La Trobe University 1
Technical University in Braunschweig 1
Fujitsu 1
National Chung Cheng University 1
Institute of Information Science and Technologies, Pisa 1
AT&T Laboratories Florham Park 1
Vrije Universiteit Amsterdam 1
Alcatel-Lucent Bell Labs 1
Federal University of Mato Grosso do Sul 1
Colorado State University 1
University of Vermont 1
National Research Council Canada 1
University of Minnesota Duluth 1
Harvard University 1
University of Central Oklahoma 1
Cairo University 1
Santa Clara University 1
University of Newcastle, Australia 1
HP Labs 1
Dartmouth College 1
Queensland University of Technology 1
Ohio University Athens 1
University of Dublin, Trinity College 1
Technical University of Darmstadt 1
King's College London 1
University of Wollongong 1
Robert Gordon University 1
University of Turin 1
Fraunhofer Institute for Integrated Publication and Information Systems Institute 1
Lehigh University 2
RAND Corporation 2
Queens College, City University of New York 2
University of Illinois 2
Mayo Clinic 2
Tel Aviv University 2
NASA Goddard Space Flight Center 2
University of Michigan-Dearborn 2
The University of Hong Kong 2
Hebrew University of Jerusalem 2
University of Sao Paulo 2
CNRS Centre National de la Recherche Scientifique 2
University of Maine 2
Tektronix Incorporated 2
CSIRO Mathematical and Information Sciences 2
University of Montreal 2
Tampere University of Technology 2
GM Research Lab 2
Fudan University 2
Avaya Inc. 2
Worcester Polytechnic Institute 2
Center for Mathematics and Computer Science - Amsterdam 2
Technical University of Berlin 2
BBN Technologies 2
Open University 2
INRIA Rhone-Alpes 2
National Chiao Tung University Taiwan 2
King Saud University 2
University of Texas at Austin 2
University of Massachusetts System 2
Drexel University 2
National Cheng Kung University 2
University of Louisiana at Lafayette 2
Weizmann Institute of Science Israel 2
The University of North Carolina System 2
University of Wisconsin Milwaukee 2
University of Calabria 2
Xerox Research Centre Europe 2
University of Delaware 2
Waseda University 2
University of Iowa 2
University of Edinburgh 2
University of California, Los Angeles 2
University of Bologna 2
Universidad Europea de Madrid 2
Northeastern University 2
Ludwig Maximilian University of Munich 2
University of California, Santa Barbara 2
University of California, Riverside 2
University of Cambridge 2
Universite d'Avignon et des Pays du Vaucluse 2
California Institute of Technology 2
INRIA Rocquencourt 2
University of Sunderland 2
Shahid Beheshti University 2
Tunis-El Manar University 2
Max Planck Institute for Informatics 3
University of Essex 3
Los Alamos National Laboratory 3
Google Inc. 3
University of Mannheim 3
The University of North Carolina at Charlotte 3
Universitat Politecnica de Catalunya 3
Australian National University 3
University of Kansas 3
University of Texas at Dallas 3
Kyoto University 3
Georgia State University 3
Ca' Foscari University of Venice 3
Beihang University 3
University of Duisburg-Essen 3
Academia Sinica Taiwan 3
Carleton University 3
Hong Kong Institute of Education 3
Vienna University of Technology 3
Southwest University of Finance and Economics China 3
Tianjin University 3
Monash University 3
University of Reggio Calabria 3
Hewlett-Packard 3
Indiana University 3
Queen Mary, University of London 3
University College London 3
New Jersey Institute of Technology 3
Georgetown University 3
Dublin City University 3
University of Sydney 3
Universidad Autonoma de Madrid 3
University of Oxford 3
Ben-Gurion University of the Negev 3
University of Illinois at Chicago 3
University of Miami 3
University of Bath 3
Syracuse University 3
University of California, San Diego 3
Shandong University 3
University of Nevada, Las Vegas 3
Pontifical Catholic University of Rio de Janeiro 3
Naval Postgraduate School 3
Nanjing University 3
National Center for Biotechnology Information 3
Aalto University 3
Federal University of Amazonas 4
University of Geneva 4
National Taiwan University 4
Delft University of Technology 4
University of Florida 4
Boston University 4
Binghamton University State University of New York 4
The University of Warwick 4
University of Strathclyde 4
Karlsruhe Institute of Technology, Campus South 4
EURECOM Ecole d'Ingenieurs & Centre de Recherche en Systemes de Communication 4
Columbia University 4
University of Copenhagen 4
York University Canada 4
Fondazione Ugo Bordoni 4
American University of Beirut 4
Microsoft Research Cambridge 4
University of Illinois at Urbana-Champaign 5
University of Sheffield 5
University of Minnesota System 5
The University of British Columbia 5
The University of Western Ontario 5
Polytechnic Institute of Turin 5
Brno University of Technology 5
City University of Hong Kong 5
University of Southern California, Information Sciences Institute 5
University of Pittsburgh 5
Nankai University 5
Simon Fraser University 5
Shanghai Jiaotong University 5
University of Utah 5
IBM Haifa Labs 5
Pohang University of Science and Technology 5
University of Roma La Sapienza 5
University of Lugano 6
Duke University 6
Brown University 6
University of Washington Seattle 6
Xerox Corporation 6
University of Twente 6
Sun Yat-Sen University 6
National Chiayi University 6
Johannes Kepler University Linz 6
University of Freiburg 6
University of Chicago 6
University of Alberta 6
University at Buffalo, State University of New York 6
University of Jyvaskyla 6
University of Padua 7
National Library of Medicine 7
Ohio State University 7
University of California, Irvine 7
Purdue University 7
Universidad de Chile 7
Bilkent University 7
Italian National Research Council 7
University of Virginia 7
University of California, Berkeley 7
University of Dortmund 7
Institute for Infocomm Research, A-Star, Singapore 8
IBM Almaden Research Center 8
Singapore Management University 8
Georgia Institute of Technology 8
IBM 8
University of Technology Sydney 8
Cardiff University 8
Federal University of Minas Gerais 9
Rutgers, The State University of New Jersey 9
Michigan State University 9
Illinois Institute of Technology 10
Arizona State University 10
Hong Kong Polytechnic University 10
Indian Statistical Institute, Kolkata 10
The University of North Carolina at Chapel Hill 10
Tsinghua University 10
University of Milan 10
Pennsylvania State University 11
Nanyang Technological University 11
New York University 11
Universidad de A Coruna 11
Istituto di Scienza e Tecnologie dell'Informazione A. Faedo 11
Hong Kong University of Science and Technology 12
Renmin University of China 12
Cornell University 13
Texas A and M University 13
Telcordia Technologies, Inc. 13
Microsoft 13
University of Melbourne 13
Virginia Tech 13
University of Southern California 14
Microsoft Research Asia 14
University of Colorado at Boulder 15
University of Amsterdam 15
Carnegie Mellon University 15
University of Waterloo 16
University of Toronto 16
University of Minnesota Twin Cities 17
University of Glasgow 18
University of Southampton 18
Peking University 19
Microelectronics Computer Technology Corporation 20
Technion - Israel Institute of Technology 20
Politecnico di Milano 20
Yahoo Research Labs 20
Stanford University 22
University of Tampere 23
RMIT University 23
Microsoft Research 23
University of Massachusetts Amherst 24
Massachusetts Institute of Technology 24
Palo Alto Research Center 24
Chinese University of Hong Kong 25
University of Queensland 26
National University of Singapore 27
University of Arizona 28
IBM Thomas J. Watson Research Center 33
University of Maryland 34

ACM Transactions on Information Systems (TOIS)
Archive


2016
Volume 35 Issue 2, October 2016  Issue-in-Progress
Volume 35 Issue 1, October 2016
Volume 34 Issue 4, September 2016
Volume 34 Issue 3, May 2016 Special Issue on Trust and Veracity of Information in Social Media
Volume 34 Issue 2, April 2016

2015
Volume 34 Issue 1, October 2015
Volume 33 Issue 4, May 2015
Volume 33 Issue 3, March 2015
Volume 33 Issue 2, February 2015
Volume 33 Issue 1, March 2015 Special Issue on Contextual Search and Recommendation

2014
Volume 32 Issue 4, October 2014
Volume 32 Issue 3, June 2014
Volume 32 Issue 2, April 2014
Volume 32 Issue 1, January 2014

2013
Volume 31 Issue 4, November 2013
Volume 31 Issue 3, July 2013
Volume 31 Issue 2, May 2013
Volume 31 Issue 1, January 2013

2012
Volume 30 Issue 4, November 2012
Volume 30 Issue 3, August 2012
Volume 30 Issue 2, May 2012
Volume 30 Issue 1, February 2012

2011
Volume 29 Issue 4, December 2011
Volume 29 Issue 3, July 2011
Volume 29 Issue 2, April 2011

2010
Volume 29 Issue 1, December 2010
Volume 28 Issue 4, November 2010
Volume 28 Issue 3, June 2010
Volume 28 Issue 2, May 2010
Volume 28 Issue 1, January 2010

2009
Volume 27 Issue 4, November 2009
Volume 27 Issue 3, May 2009
Volume 27 Issue 2, February 2009

2008
Volume 27 Issue 1, December 2008
Volume 26 Issue 4, September 2008
Volume 26 Issue 3, June 2008
Volume 26 Issue 2, March 2008

2007
Volume 26 Issue 1, November 2007
Volume 25 Issue 4, October 2007
Volume 25 Issue 3, July 2007
Volume 25 Issue 2, April 2007
Volume 25 Issue 1, February 2007

2006
Volume 24 Issue 4, October 2006
Volume 24 Issue 3, July 2006
Volume 24 Issue 2, April 2006
Volume 24 Issue 1, January 2006

2005
Volume 23 Issue 4, October 2005
Volume 23 Issue 3, July 2005
Volume 23 Issue 2, April 2005
Volume 23 Issue 1, January 2005

2004
Volume 22 Issue 4, October 2004
Volume 22 Issue 3, July 2004
Volume 22 Issue 2, April 2004
Volume 22 Issue 1, January 2004

2003
Volume 21 Issue 4, October 2003
Volume 21 Issue 3, July 2003
Volume 21 Issue 2, April 2003
Volume 21 Issue 1, January 2003

2002
Volume 20 Issue 4, October 2002
Volume 20 Issue 3, July 2002
Volume 20 Issue 2, April 2002
Volume 20 Issue 1, January 2002

2001
Volume 19 Issue 4, October 2001
Volume 19 Issue 3, July 2001
Volume 19 Issue 2, April 2001
Volume 19 Issue 1, Jan. 2001

2000
Volume 18 Issue 4, Oct. 2000
Volume 18 Issue 3, July 2000
Volume 18 Issue 2, April 2000
Volume 18 Issue 1, Jan. 2000

1999
Volume 17 Issue 4, Oct. 1999
Volume 17 Issue 3, July 1999
Volume 17 Issue 2, April 1999
Volume 17 Issue 1, Jan. 1999

1998
Volume 16 Issue 4, Oct. 1998
Volume 16 Issue 3, July 1998
Volume 16 Issue 2, April 1998
Volume 16 Issue 1, Jan. 1998

1997
Volume 15 Issue 4, Oct. 1997
Volume 15 Issue 3, July 1997
Volume 15 Issue 2, April 1997
Volume 15 Issue 1, Jan. 1997

1996
Volume 14 Issue 4, Oct. 1996
Volume 14 Issue 3, July 1996
Volume 14 Issue 2, April 1996
Volume 14 Issue 1, Jan. 1996

1995
Volume 13 Issue 4, Oct. 1995 Special issue on video information retrieval
Volume 13 Issue 3, July 1995
Volume 13 Issue 2, April 1995
Volume 13 Issue 1, Jan. 1995

1994
Volume 12 Issue 4, Oct. 1994
Volume 12 Issue 3, July 1994
Volume 12 Issue 2, April 1994 Special issue on social science perspectives on IS
Volume 12 Issue 1, Jan. 1994

1993
Volume 11 Issue 4, Oct. 1993
Volume 11 Issue 3, July 1993
Volume 11 Issue 2, April 1993
Volume 11 Issue 1, Jan. 1993

1992
Volume 10 Issue 4, Oct. 1992 Special issue on user interface software and technology
Volume 10 Issue 3, July 1992
Volume 10 Issue 2, April 1992
Volume 10 Issue 1, Jan. 1992

1991
Volume 9 Issue 4, Oct. 1991
Volume 9 Issue 3, July 1991 Special issue on research and development in information retrieval
Volume 9 Issue 2, April 1991 Special issue on computer—human interaction
Volume 9 Issue 1, Jan. 1991

1990
Volume 8 Issue 4, Oct. 1990
Volume 8 Issue 3, July 1990
Volume 8 Issue 2, Apr. 1990
Volume 8 Issue 1, Jan. 1990

1989
Volume 7 Issue 4, Oct. 1989
Volume 7 Issue 3, July 1989
Volume 7 Issue 2, April 1989
Volume 7 Issue 1, Jan. 1989

1988
Volume 6 Issue 4, Oct. 1988
Volume 6 Issue 3, July 1988
Volume 6 Issue 2, April 1988
Volume 6 Issue 1, Jan. 1988

1987
Volume 5 Issue 4, Oct. 1987
Volume 5 Issue 3, July 1987
Volume 5 Issue 2, April 1987
Volume 5 Issue 1, Jan. 1987

1986
Volume 4 Issue 4, Oct. 1986
Volume 4 Issue 3, July 1986 Special issue: selected papers from the conference on office information systems
Volume 4 Issue 2, April 1986
Volume 4 Issue 1, Jan. 1986

1985
Volume 3 Issue 4, Oct. 1985
Volume 3 Issue 3, July 1985
Volume 3 Issue 2, April 1985
Volume 3 Issue 1, Jan. 1985

1984
Volume 2 Issue 4, Oct. 1984
Volume 2 Issue 3, July 1984
Volume 2 Issue 2, April 1984
Volume 2 Issue 1, Jan. 1984

1983
Volume 1 Issue 4, Oct. 1983
Volume 1 Issue 3, July 1983
Volume 1 Issue 2, April 1983
Volume 1 Issue 1, Jan. 1983
 
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