Transactions on Intelligent Systems and Technology
ACM Transactions on Intelligent Systems and Technology (ACM TIST, 2014
Impact Factor: 9.39) is a scholarly journal that publishes the highest
quality papers on intelligent systems, applicable algorithms and
technology with a multi-disciplinary perspective. An intelligent system is
one that uses artificial intelligence (AI) techniques to offer important
services (e.g., as a component of a larger system) to allow integrated
systems to perceive, reason, learn, and act intelligently in the real
world.
ACM TIST is published quarterly (four issues a year). Each issue has 5-8 regular papers, with around 20 published journal pages or 10,000 words per paper. Additional references, proofs, graphs or detailed experiment results can be submitted as a separate appendix, while excessively lengthy papers will be rejected automatically. Authors can include online-only appendices for additional content of their published papers and are encouraged to share their code and/or data with other readers. Published articles can be accessed through the
ACM Digital
Library. To facilitate open access, meta data on the published papers can be freely accessed. Authors can
post their accepted manuscripts and supplementary material online for others to download.
SCI Expanded Index (2014 IF: 9.39) and EI Index (ISSN:2157-6904).
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 author-pays option which allows
for perpetual open access through the ACM Digital Library. For more
information, visit the ACM Author Rights webpage at http://authors.acm.org.
Call for Nominations of Editor-In-Chief [PDF] [WORD]
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Featured Articles
Simple and Scalable Response Prediction for Display Advertising
(published in Vol.5, No.4)
Olivier Chapelle(1), Eren Manavoglu(2), , Romer Rosales(3)
(1) Criteo
(2) Microsoft
(3) LinkedIn
Clickthrough and conversation rates estimation are two core predictions tasks in display advertising. We present in this article a machine learning framework based on logistic regression that is specifically designed to tackle the specifics of display advertising. The resulting system has the following characteristics: It is easy to implement and deploy, it is highly scalable (we have trained it on terabytes of data), and it provides models with state-of-the-art accuracy. (Read more)
Factorization Machines with libFM
(published in Vol.3, No.3)
Steffen Rendle(1)
(1) University of Konstanz
Factorization approaches provide high accuracy in several important prediction problems, for example, recommender systems. However, applying factorization approaches to a new prediction problem is a nontrivial task and requires a lot of expert knowledge. Typically, a new model is developed, a learning algorithm is derived, and the approach has to be implemented. Factorization machines (FM) are a generic approach since they can mimic most factorization models just by feature engineering. This way, factorization machines combine the generality of feature engineering with the superiority of factorization models in estimating interactions between categorical variables of large domain. libFM is a software implementation for factorization machines that features stochastic gradient descent (SGD) and alternating least-squares (ALS) optimization, as well as Bayesian inference using Markov Chain Monto Carlo (MCMC). This article summarizes the recent research on factorization machines both in terms of modeling and learning, provides extensions for the ALS and MCMC algorithms, and describes the software tool libFM. (Read more)
Diversifying Citation Recommendations
(published in Vol.5, No.4)
Onur Küçüktunç(1), Erik Saule(2), Kamer Kaya(1), Ümit V. Çatalyürek(1)
(1) The Ohio State University
(2) University of North Carolina at Charlotte
Literature search is one of the most important steps of academic research. With more than 100,000 papers published each year just in computer science, performing a complete literature search becomes a Herculean task. Some of the existing approaches and tools for literature search cannot compete with the characteristics of today’s literature, and they suffer from ambiguity and homonymy. Techniques based on citation information are more robust to the mentioned issues. Thus, we recently built a Web service called the advisor, which provides personalized recommendations to researchers based on their papers of interest. Since most recommendation methods may return redundant results, diversifying the results of the search process is necessary to increase the amount of information that one can reach via an automated search. This article targets the problem of result diversification in citation-based bibliographic search, assuming that the citation graph itself is the only information available and no categories or intents are known. The contribution of this work is threefold. We survey various random walk--based diversification methods and enhance them with the direction awareness property to allow users to reach either old, foundational (possibly well-cited and well-known) research papers or recent (most likely less-known) ones. Next, we propose a set of novel algorithms based on vertex selection and query refinement. A set of experiments with various evaluation criteria shows that the proposed γ-RLM algorithm performs better than the existing approaches and is suitable for real-time bibliographic search in practice.
(Read more)
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