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Query Performance Prediction Using Reference Lists

The task of query performance prediction is to estimate the effectiveness of search performed in response to a query when no relevance judgments are... (more)

Comparing Pointwise and Listwise Objective Functions for Random-Forest-Based Learning-to-Rank

Current random-forest (RF)-based learning-to-rank (LtR) algorithms use a classification or regression framework to solve the ranking problem in a... (more)

Probabilistic Models for Contextual Agreement in Preferences

The long-tail theory for consumer demand implies the need for more accurate personalization technologies to target items to the users who most desire... (more)

Examining Additivity and Weak Baselines

We present a study of which baseline to use when testing a new retrieval technique. In contrast to past work, we show that measuring a statistically significant improvement over a weak baseline is not a good predictor of whether a similar improvement will be measured on a strong baseline. Sometimes strong baselines are made worse when a new... (more)

Measuring the Semantic Uncertainty of News Events for Evolution Potential Estimation

The evolution potential estimation of news events can support the decision making of both corporations and governments. For example, a corporation... (more)

Diversifying Query Auto-Completion

Query auto-completion assists web search users in formulating queries with a few keystrokes, helping them to avoid spelling mistakes and to produce clear query expressions, and so on. Previous work on query auto-completion mainly centers around returning a list of completions to users, aiming to push queries that are most likely intended by the... (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
A Probabilistic Lifestyle-based Trajectory Model for Social Strength Inference from Human Trajectory Data

In this paper, we propose a probabilistic generative model to mine latent lifestyle-related patterns from human trajectory data for inferring social strength. It can automatically learn functionality topics consisting of locations with similar service functions and transition probabilities over the set of functionality topics. Furthermore, a lifestyle is modeled as a unique transition probability matrix over the set of functionality topics. A user has a preference distribution over the set of lifestyles, and she is able to select over multiple lifestyles to adapt to different living contexts. The learnt lifestyle-related patterns are subsequently used as features in a supervised learner for both strength estimation and link prediction. We conduct extensive experiments to evaluate the performance of the proposed method on two real-world datasets. The experimental results demonstrate the effectiveness of our proposed method.

Ranking-oriented Collaborative Filtering: A Listwise Approach

Conventional memory-based ranking-oriented Collaborative filtering (CF) can be referred to as pairwise algorithms. They represent each user as a set of preferences on each pair of items for similarity calculations and predictions. In this study, we propose ListCF, a novel listwise CF algorithm that seeks improvement in both accuracy and efficiency. In ListCF, each user is represented as a probability distribution of the permutations over rated items based on the Plackett-Luce model, and the similarity between users is measured based on the Kullback-Leibler (KL) divergence between their probability distributions over the set of commonly rated items. Given a target user and the most similar users, ListCF directly predicts a total order of items for each user based on similar users' probability distributions over permutations of the items. Besides, we also reveal insightful connections among pointwise, pairwise and listwise CF algorithms from the perspective of the matrix representations. In addition, to make our algorithm more scalable, we present an incremental algorithm for ListCF, which allows to incrementally update the similarities between users when certain user submits a new rating or updates an existing rating. Extensive experiments on benchmark datasets in comparison with the state-of-the-art approaches demonstrate the promise of our approach.

Cross-Lingual Topic Discovery From Multilingual Search Engine Query Log

Nowadays major commercial search engines are operating in a multinational fashion to provide web search services for millions of users who compose search queries by different languages. Hence, search engine query log, which serves as the backbone of many search engine applications, records millions of users search history in a wide spectrum of human languages and demonstrates a strong multilingual phenomenon. However, with its salience, the multilingual nature of search engine query log is usually ignored by the existing works, which usually consider query log entries of different languages as being orthogonal and independent. This kind of oversimplified assumption heavily distorts the underlying structure of web search data. In this paper, we pioneer in recognition of the multilingual nature of query log and make the first attempt to cross the language barrier in query log. Both qualitative and quantitative experimental results show that the CL-QLM is able to effectively derive cross-lingual topics from multilingual query log and spawn a wide spectrum of new search engine applications.

Context Trees: Augmenting Geospatial Trajectories with Context

Exposing latent knowledge in geospatial trajectories has the potential to provide a greater level of understanding of individuals and groups than previously afforded. Motivated by a desire to understand the movement patterns of users, this work explores the potential offered by trajectories augmented with land usage information to understand the context behind user actions. The resultant context tree, a hierarchical data structure generated through novel filtering and clustering techniques, summarises historic contexts of the user in a manner that entails rapid access to information previously latent within the original trajectories. Context trees, combined with a technique for pruning that aims to reduce complexity with minimal loss of information, provide a foundation for understanding and modelling the behaviours of individuals and groups. The filtering, clustering and pruning stages of the proposed process, along with context trees generated from real-world data, are evaluated relative to desirable properties to demonstrate their utility.

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.

Accounting for Language Changes over Time in Document Similarity Search

Given a query document, ranking the documents in a collection based on how similar they are to the query is an essential task with extensive applications. For collections that contain documents whose creation dates span several decades, this task is further complicated by the fact that the language changes over time. For example, many terms add or lose one or more senses to meet people's evolving needs. To address this problem, we present methods that take advantage of two types of information to account for the language change. The first is the citation network that often exists within the collection, which can be used to link related documents with significantly different creation dates (and hence different language use). The second is the changes in the usage frequency of terms that occur over time, which can indicate changes in their senses and uses. These methods utilize the above information while estimating the representation of both documents and terms within the context of non-probabilistic static and dynamic topic models. Our experiments on two real-world datasets that span more than 40 years show that our proposed methods improve the retrieval performance of existing models and that these improvements are statistically significant.

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.

Social Question Answering: Textual, User, and Network Features for Best Answer Prediction

Community Question Answering (CQA) sites use a collaborative paradigm to satisfy complex information needs. Although the task of matching questions to their best answers has been tackled for more than a decade, the social question answering practice is a complex process. The factors inuencing the accuracy of question-answer matching are many and hard to disentangle. We approach the task from an application-oriented perspective, probing the space of several dimensions relevant to this problem: features, algorithms, and topics. We gather under a learning-to-rank framework the most extensive feature set used in literature to date, including 225 features from ve different families. We test the power of such features in predicting the best answer to a question on the largest dataset from Yahoo Answers used for this task so far (40M answers), and provide a faceted analysis of the results along different topical areas and question types. We propose a novel family of distributional semantics measures that can most of the times seamlessly replace widely used linguistic similarity features,being more than one order of magnitude faster to compute and providing greater predictive power. The best feature set reaches an improvement between 11% and 26% in P@1 compared to recent well-established state-of-the-art methods.

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).

Trip Recommendation Meets Real World Constraints: POI Availability, Diversity and Traveling Time Uncertainty

As location-based social network (LBSN) services become increasingly popular, trip recommendation that recommends a sequence of points of interest (POIs) to visit for a user emerges as one of many important applications of LBSNs. Personalized trip recommendation tailors to users' specific tastes by learning from past check-in behaviors of users and their peers. Finding the optimal trip that maximizes user's experiences for a given time budget constraint is an NP hard problem and previous solutions do not consider two practical and important constraints. One constraint is POI availability where a POI may be only available during a certain time window. Another constraint is uncertain traveling time where the traveling time between two POIs is uncertain. Besides, the diversity of the POIs included in the trip also affect user's adoptions. This work presents efficient solutions to personalized trip recommendation by incorporating these constraints to prune the search space. We evaluated the efficiency and effectiveness of our solutions on real life LBSN data sets.

TopPRF: A Probabilistic Framework for Integrating Topic Space into Pseudo Relevance Feedback

Recently, topic modeling becomes more and more popular in the information retrieval (IR) and other related areas. In this paper, we first investigate a key factor of the fuzzy topic problem, the topic number for topic modeling and how it makes topics "fuzzy". A new concept Topic Space (TS) is also introduced to evaluate the reliability of each candidate feedback document. Then we propose a probabilistic framework "TopPRF" and three new models TS-COS, TS-EU and TS-Entropy via integrating "Topic Space information into Pseudo Relevance Feedback (PRF). The proposed framework and models will identify how reliable a document is to be relevant through both term and topic information, which allow us to search documents beyond traditional term matching only and provide a promising avenue for constructing better topic-space based IR systems. When selecting feedback terms, candidate terms in more reliable feedback documents should obtain extra weights. Extensive experiments on various public collections justify that our proposed methods can significantly reduce the influence of "fuzzy topics and also make significant improvements over strong baseline models with optimal parameter settings.

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.

Boosting Recommendation in Unexplored Categories by User Price Preference

State-of-the-art methods for product recommendation encounter significant performance drop in categories where a user has no purchase history. This problem needs to be addressed since current online retailers are moving beyond single category and attempting to be diversified. In this paper, we investigate the challenging problem of product recommendation in unexplored categories and discover that the price, a factor comparable across categories, can improve the recommendation performance significantly. We introduce the price utility concept to characterize users' sense of price and propose three different utility functions. We show that user price preference in a category is a distribution and we mine typical user price preference patterns based on three different types of distance between distributions. We fuse user price preference through regularization and joint factorization to boost recommendation performance in both browsing and buying shopping orientations. Experimental results show that fusing user price preference improves performance in a series of recommendation tasks: unexplored category recommendation, product recommendation under a given unexplored category and product recommendation under generic unexplored categories.

Answering Arabic Why-questions: Baseline vs. RST-based Approach

Question answering (QA) system is concerned with building a system that automatically answer questions posed by humans in a natural language. Compared to other languages, little effort was directed towards QA systems for Arabic. Due to the difficulty of handling why-questions, most Arabic QA systems tend to ignore it. In this paper, we specifically address the why-question for Arabic using two different approaches and compare their performance and the quality of their answer. The first approach is the baseline approach, and for the second approach we use Rhetorical Structure Theory (RST). We evaluate both schemes using a corpora of 700 textual documents in different genres collected from Open Source Arabic Corpora (OSAC), and a set of 100 question-answer pairs. Of these, we had 45 common questions where both schemes answered correctly, 19 of which were identically answered. For the remaining 26 questions, the RST-based approach managed better answers for over half the questions. Overall the RST-based approach achieved better recall and precision.

The Effects of Aggregated Search Coherence on Search Behavior

Aggregated search is the task of combining results from multiple independent search systems in a single search results page (SERP). Aggregated search coherence refers to the extent to which different sources on the SERP focus on similar senses of an ambiguous query. In previous studies, we found that the query-senses in a set of vertical results can influence user engagement with the web results (the so-called ``spillover'' effect). In this work, we investigate five research questions (RQ1-RQ5) that extend our prior work. First, we investigate the extent to which results from different sources focus on different senses of an ambiguous query (RQ1). Second, we investigate how the vertical-to-web spillover effect varies across different verticals (RQ2). Then, we examine whether the level of spillover depends on the vertical position (RQ3) and on whether the vertical results are displayed with a border and different-colored background to distinguish them from the web results (RQ4). Finally, we propose a new method for displaying results from a particular vertical that are more consistent with the query-senses in the web results (RQ5). We evaluate this new method based on how it influences users to make more correct decisions about their level of engagement with the web results.

Bibliometrics

Publication Years 1983-2016
Publication Count 633
Citation Count 19607
Available for Download 633
Downloads (6 weeks) 3640
Downloads (12 Months) 35409
Downloads (cumulative) 720145
Average downloads per article 1138
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)
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)
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)
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-Infosys Foundation Award in the Computing Sciences (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)
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 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)
Qiang Yang ACM Distinguished Member (2011)
Shumin Zhai ACM Distinguished Member (2006)

First Name Last Name Paper Counts
Tatseng Chua 8
Justin Zobel 8
William Croft 8
Hsinchun Chen 8
Norbert Fuhr 7
Stavros Christodoulakis 6
Hector Garcia-Molina 6
Bin Cui 6
Gonzalo Navarro 5
Kalervo Järvelin, 5
Oren Kurland 5
Dennis Tsichritzis 5
Alistair Moffat 5
John Gould 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
Zi Huang 4
Fabio Crestani 4
ChengXiang Zhai 4
Kamfai Wong 4
Jimmy Lin 4
Hengtao Shen 4
Mark Sanderson 3
Swapan Parui 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
Heikki Keskustalo 3
Kumyew Lai 3
Nieves Brisaboa 3
Luo Si 3
Matthias Jarke 3
Christos Faloutsos 3
Raffaele Perego 3
Fabrizio Silvestri 3
Henry Gladney 3
Ge Nong 3
Roy Rada 3
Kuilam Kwok 3
Robert Luk 3
Marjan Celikik 3
Nívio Ziviani 3
David Carmel 3
Eric Horvitz 3
Abraham Bookstein 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
Darrell Woelk 2
Ronny Lempel 2
Zhiting Hu 2
Raymond Panko 2
Robert Krovetz 2
Warren Greiff 2
Seungwon Hwang 2
Vijay Raghavan 2
Mark Chignell 2
Rong Jin 2
Charles Wiecha 2
Alan Hevner 2
Fazli Can 2
Eric Jensen 2
Panagiotis Ipeirotis 2
Sakti Pramanik 2
Jiaul Paik 2
Mandar Mitra 2
Essam El-Kwae 2
John Carroll 2
Hochung Wu 2
Loren Terveen 2
Carson Woo 2
David Hicks 2
Anna Shtok 2
Michael Lyu 2
Jialie Shen 2
Howard Turtle 2
Qiang Zhu 2
Iadh Ounis 2
Wanda Orlikowski 2
Kotagiri Ramamohanarao 2
Fernando Diaz 2
William Webber 2
Hong Cheng 2
Barbara Pernici 2
Carmel Domshlak 2
Leefeng Chien 2
Antonio Fariña, 2
Clayton Lewis 2
Zhaohui Zheng 2
Gediminas Adomavicius 2
Shibing Yao 2
Jinxi Xu 2
Clyde Giles 2
Sudha Ram 2
Padmini Srinivasan 2
Weiyi Meng 2
Jaime Teevan 2
Raymond Lau 2
Fred Damerau 2
Keke Chen 2
Laurence Park 2
Nick Craswell 2
Nigel Shadbolt 2
Lucy Suchman 2
Antonio Siochi 2
Steven Beitzel 2
Elena Baralis 2
Paolo Garza 2
Scott Hudson 2
Joemon Jose 2
Christopher Higgins 2
Mansur Kabuka 2
Nicole Yankelovich 2
Edward Dang 2
Jianyun Nie 2
Chirag Shah 2
Claudio Carpineto 2
Shane Culpepper 2
Waihong Chan 2
Nat Ballou 2
Shipeng Li 2
Hui Yang 2
Gautam Pant 2
Jonathan Grudin 2
Hui Xiong 2
Benjamin Piwowarski 2
Filip Radlinski 2
Lillian Lee 2
Ian Ruthven 2
Stefan Siersdorfer 2
John Riedl 2
Frank Tompa 2
Özgür Ulusoy 2
Frederick Lochovsky 2
David Vallet 2
Xiaojun Wan 2
William Jones 2
Peter Bruza 2
Mounia Lalmas 2
Gordon Cormack 2
Jaana Kekäläinen 2
Arjen De Vries 2
Yoelle Maarek 2
Dou Shen 2
John Mylopoulos 2
George Fitzmaurice 2
Olivia Sheng 2
Anthony Kaye 2
Paul Stotts 2
Qiang Yang 2
John Schnase 2
Douglas Terry 2
Jennifer Widom 2
Ismail Altingövde 2
Ronan Cummins 2
Maria Fugini 2
Tao Mei 2
Javed Mostafa 2
Quan Yuan 2
Gao Cong 2
Hongzhi Yin 2
Pasquale Savino 2
Hady Lauw 2
Eepeng Lim 2
Hongyan Liu 2
Stephen Boies 2
Richard Furuta 2
Jiantao Sun 2
Hideki Koike 2
Massimo Melucci 2
Salvatore Orlando 2
Xiaofang Zhou 2
Susan Gauch 2
Edward Fox 2
Irwin King 2
Pável Calado 2
Tao Tao 2
Manolis Koubarakis 2
Neil Rowe 2
Randall Trigg 2
Letizia Tanca 2
Marcel Reinders 2
Dimitris Papadias 2
Giovanni Romano 2
Jorge Garza 2
Won Kim 2
Stanley Zdonik 2
Berthier Ribeiro-Neto 2
Piero Fraternali 2
Paolo Paolini 2
Sanda Harabagiu 2
Ling Feng 2
Brian Cooper 2
Daniel Zeng 2
Mark Carman 2
Stuart Middleton 2
David Hawking 2
Thorsten Joachims 2
Hongtai Chou 1
David Maier 1
Shiyang Lu 1
Anders Bjornerstedt 1
Andrea Esuli 1
Chengyu Hsieh 1
Ronald Lee 1
Cary Buchman 1
David Grossman 1
Akinori Yonezawa 1
Richard Watson 1
Gaewon You 1
Youngin Song 1
Zaiqing Nie 1
Steven Miller 1
Venkat Rangan 1
Katsumi Tanaka 1
Hazem Hajj 1
Michael Littman 1
Bernard Jansen 1
Mark Mydland 1
Sergey Melnik 1
Jonathan Gibbons 1
Lihong Li 1
Christopher Fry 1
Christopher Shaw 1
Mihai Lupu 1
Keld Bødker 1
Anders Mørch 1
Vasileios Hatzivassiloglou 1
Ulla Merz 1
Helene Hembrooke 1
Hang Cui 1
David Rosenblitt 1
Mayer Schwartz 1
Gregg Foster 1
Tieyan Liu 1
L Leung 1
Muhammad Ibrahim 1
Xiaozhao Zhao 1
John Shepherd 1
Noa Gradovitch 1
Amjad Daoud 1
Lenwood Heath 1
Max Van Kleek 1
Manuel De Buenaga 1
Timothy Hoad 1
Hang Li 1
Sanghoon Lee 1
Gianni Costa 1
Hao Chen 1
Elihu Gerson 1
Shlomo Moran 1
Holger Meuss 1
Yisong Yue 1
Saturnino Luz 1
Dong Wang 1
Robert Mack 1
Riccardo Miotto 1
F Campagnoni 1
Hamideh Afsarmanesh 1
J Rames 1
Ling Chen 1
Xiaofang Zhou 1
Luca Becchetti 1
Jon Kleinberg 1
Casper Petersen 1
Minghao Piao 1
Olivier Van Laere 1
Ling Chen 1
George Awad 1
Wessel Kraaij 1
Miro Lehtonen 1
ManSaint Yuen 1
Gergely Korodi 1
Shahana Sen 1
Mark Ackerman 1
Aditya Pal 1
Franklin Harper 1
Daniel Lemire 1
Jirong Wen 1
Tiejun Huang 1
Christopher Jones 1
E Fiume 1
Mark Hornick 1
Alan Purdy 1
George Karypis 1
Mukund Deshpande 1
Shengyang Li 1
Edward Sciore 1
Diklun Lee 1
Ralf Güting 1
Margrethe Olson 1
Michael Graves 1
Jinsoo Park 1
Alan Biermann 1
Jing Bai 1
Deborah Hix 1
Debra Gash 1
Jie Han 1
Yannis Drougas 1
Gerald Karam 1
Lise Getoor 1
Gunter Saake 1
Ravi Krishnamurthy 1
Anthony Tomasic 1
Mehran Sahami 1
Joanne Claussen 1
Peter Schwarz 1
Thomas Landauer 1
Louis Gomez 1
Mark Weiser 1
Engin Demir 1
Stefano Mizzaro 1
Dario Lucarella 1
Peter Schäuble 1
José San Pedro 1
Tim Pohle 1
Shimon Schocken 1
Murray Mazer 1
Elisa Quintarelli 1
Xiaofeng Guan 1
Kuochu Lee 1
Author index 1
Shaul Markovitch 1
Bo Yang 1
Xiansheng Hua 1
Walid Magdy 1
Il Im 1
Yangchang Hong 1
Robert Nicholson 1
M Rashidian 1
Sheng Gao 1
Shiqiang Yang 1
Marianne Storrøsten 1
Michal Rosen-Zvi 1
Chaitanya Chemudugunta 1
Helen Nissenbaum 1
Catherine Plaisant 1
Ying Lu 1
Michael Cole 1
Sanna Kumpulainen 1
Satya Rachakonda 1
Carolyn Stanley 1
Yeyi Wang 1
Mstislav Maslennikov 1
Hongjun Lu 1
Daniel Berry 1
Norbert Janssen 1
Yimin Wei 1
Aldong Zhang 1
Yannis Vassiliou 1
Hiroshi Kuribayashi 1
Rajiv Choudhary 1
Richard Taylor 1
Siddharth Kaza 1
Shima Gerani 1
John Limb 1
Sargur Srihari 1
Adam Jatowt 1
Roula Hobeica 1
Caroline Eastman 1
Christine Kincaid 1
Stephen Weyer 1
Joyce Chai 1
Patrick Gallinari 1
Robert DeLine 1
Pierre Dupont 1
Zonghuan Wu 1
Cody Kwok 1
Symeon Papadopoulos 1
Jørgen Bansler 1
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Affiliation Paper Counts
Polytechnic Institute of Bari 1
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Enel 1
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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
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
Simon Fraser University 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
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
Shanghai Jiaotong 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
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
IBM 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
University of Jyvaskyla 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
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
Renmin University of China 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 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
University of Technology Sydney 8
Cardiff University 8
The University of North Carolina at Chapel Hill 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
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
Cornell University 13
Texas A and M University 13
Telcordia Technologies, Inc. 13
Microsoft 13
University of Melbourne 13
Virginia Tech 13
Microsoft Research Asia 13
University of Southern California 14
University of Colorado at Boulder 15
University of Amsterdam 15
Carnegie Mellon University 15
University of Minnesota Twin Cities 15
University of Waterloo 16
University of Toronto 16
University of Glasgow 18
Peking University 18
University of Southampton 18
Microelectronics Computer Technology Corporation 20
Technion - Israel Institute of Technology 20
Politecnico di Milano 20
Yahoo Research Labs 20
University of Queensland 21
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
National University of Singapore 27
University of Arizona 28
IBM Thomas J. Watson Research Center 32
University of Maryland 34

ACM Transactions on Information Systems (TOIS)
Archive


2016
Volume 34 Issue 4, August 2016  Issue-in-Progress
Volume 35 Issue 1, August 2016  Issue-in-Progress
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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