Marketers evaluate recommender systems not on its algorithms but on how well the vendor's expertise and interfaces will support achieving business goals. Driven by a business model that pays based on recommendation success, vendors guide clients through continuous optimization of recommendations. While recommender technology is mature, the solutions and market are still young. As a result, solutions are not fully integrated with other business systems and technology platforms.

Recommender systems are tools for interacting with large and complex information spaces. The field, christened in 1995, has grown enormously in the variety of problems addressed and techniques employed, as well as in its practical applications. Recommender systems research has incorporated a wide variety of artificial intelligence techniques including machine learning, data mining, user modeling, case-based reasoning, and constraint satisfaction, among others. The purpose of the articles in this special issue is to take stock of the current landscape of recommender systems research and identify directions the field is now taking.

Recommender systems now play an important role in online information discovery, complementing traditional approaches such as search and navigation, with a more proactive approach to discovery that is informed by the users interests and preferences. To date recommender systems have been deployed within a variety of e-commerce domains, covering a range of products such as books, music, movies, and have proven to be a successful way to convert browsers into buyers. Recommendation technologies have a potentially much greater role to play in information discovery however and in this article we consider recent research that takes a fresh look at web search as a fertile platform for recommender systems research as users demand a new generation of search engines that are less susceptible to manipulation and more responsive to searcher needs and preferences.

Recommender systems are a means of personalizing the presentation of information to ensure that users see the items most relevant to them. The social web has added new dimensions to the way people interact on the Internet, placing the emphasis on user-generated content. Users in social networks create photos, videos and other artifacts, collaborate with other users, socialize with their friends and share their opinions online. This outpouring of material has brought increased attention to recommender systems, as a means of managing this vast universe of content. At the same time, the diversity and complexity of the data has meant new challenges for researchers in recommendation. This article describes the nature of recommendation research in social web applications and provides some illustrative examples of current research directions and techniques. It is difficult to overstate the impact of the social web. This new breed of social applications is reshaping nearly every human activity from the way people watch movies to how they overthrow governments. Facebook allows its members to maintain friendships whether they live next door or on another continent. With Twitter, users from celebrities to ordinary folks can launch their 140 character messages out to a diverse horde of ‘‘followers.” Flickr and YouTube users upload their personal media to share with the world, while Wikipedia editors collaborate on the world’s largest encyclopedia.

Recommender systems are tools for interacting with large and complex information spaces. They provide a personalized view of such spaces, prioritizing items likely to be of interest to the user. The field, christened in 1995, has grown enormously in the variety of problems addressed and techniques employed, as well as in its practical applications. Recommender systems research has incorporated a wide variety of artificial intelligence techniques including machine learning, data mining, user modeling, case-based reasoning, and constraint satisfaction, among others. Personalized recommendations are an important part of many on-line e-commerce applications such as Amazon.com, Netflix, and Pandora. This wealth of practical application experience has provided inspiration to researchers to extend the reach of recommender systems into new and challenging areas. The purpose of the articles in this special issue is to take stock of the current landscape of recommender systems research and identify directions the field is now taking. This article provides an overview of the current state of the field and introduces the various articles in the special issue.

Entity resolution is the problem of reconciling database references corresponding to the same real-world entities. Given the abundance of publicly available databases that have unresolved entities, we motivate the problem of query-time entity resolution quick and accurate resolution for answering queries over such unclean databases at query-time. Since collective entity resolution approaches --- where related references are resolved jointly --- have been shown to be more accurate than independent attribute-based resolution for off-line entity resolution, we focus on developing new algorithms for collective resolution for answering entity resolution queries at query-time. For this purpose, we first formally show that, for collective resolution, precision and recall for individual entities follow a geometric progression as neighbors at increasing distances are considered. Unfolding this progression leads naturally to a two stage expand and resolve query processing strategy. In this strategy, we first extract the related records for a query using two novel expansion operators, and then resolve the extracted records collectively. We then show how the same strategy can be adapted for query-time entity resolution by identifying and resolving only those database references that are the most helpful for processing the query. We validate our approach on two large real-world publication databases where we show the usefulness of collective resolution and at the same time demonstrate the need for adaptive strategies for query processing. We then show how the same queries can be answered in real-time using our adaptive approach while preserving the gains of collective resolution. In addition to experiments on real datasets, we use synthetically generated data to empirically demonstrate the validity of the performance trends predicted by our analysis of collective entity resolution over a wide range of structural characteristics in the data.

The 2011 AAAI Classic Paper Award was given to the authors of the most influential papers from the Tenth National Conference on Artificial Intelligence, held in 1992 in San Jose, California. The award was presented to Mitchell received his BSc in cognitive process. The winning papers were selected Hector Levesque, David Mitchell, and science and artificial intelligence at by the program chairs with the Bart Selman for their two papers, Hard the University of Toronto, his MSc in help of area chairs and members of the and Easy Distribution of SAT Problems computing science from Simon Fraser senior program committee. Honors and A New Method for Solving Hard University, and his PhD in computer went to Jessica Davies (University of Satisfiability Problems. Paris Sud 11), Nina Narodytska to the area of automated Bart Selman is a professor of computer (NICTA and University of New South reasoning via methods and analyses science at Cornell University.

This page includes forthcoming AAAI sponsored conferences, conferences presented by AAAI Affiliates, and conferences held in cooperation with AAAI. AI International Conference on Pattern Magazine also maintains a calendar listing that includes nonaffiliated conferences Recognition Applications and Methods. AIIDE-11 will be Flairs-2012 will be held May 23-25, HRI2012 will be held March 5-8, held October 11-14, 2011 at Stanford 2012, in Marco Island, Florida. ICEIS 2012 will be held June 28 Trinity College in Dublin, Ireland. ICINCO 2012 will be held March 26-28, 2012 at Stanford Third International Joint Conference held July 28-31, 2012 in Rome, Italy.

The titles of the eight symposia were Artificial Intelligence and Health Communication, Artificial Intelligence and Sustainable Design, Artificial Intelligence for Business Agility, Computational Physiology, Help Me Help You: Bridging the Gaps in Human-Agent Collaboration, Logical Formalizations of Commonsense Reasoning, Multirobot Systems and Physical Data Structures, and Modeling Complex Adaptive Systems As If They Were Voting Processes. The goal of the Artificial Intelligence and Health Communication symposium was to advance the conceptual design of automated systems that provide health services to patients and consumers through interdisciplinary insight from artificial intelligence, health communication and related areas of communication studies, discourse studies, public health, and psychology. There is a large and growing interest in the development of automated systems to provide health services to patients and consumers. In the last two decades, applications informed by research in health communication have been developed, for example, for promoting healthy behavior and for managing chronic diseases. While the value that these types of applications can offer to the community in terms of cost, access, and convenience is clear, there are still major challenges facing design of effective health communication systems. Overall, the participants found the format of the symposium engaging and constructive, and they The symposium was organized around five main expressed the desire to continue this initiative in concepts: (1) Patient empowerment and education further events.

This year, the Robotics Exhibition included two such robotics challenge problems: manipulation and learning by demonstration. In the Small-Scale Manipulation Challenge four teams demonstrated systems playing robotic chess. This exhibit was organized by David Touretzky and Monica D. Anderson. In the Learning by Demonstration Challenge, three teams demonstrated systems learning a block-sorting task. This exhibit was organized by Sonia Chernova. Additionally, this year marked another successful turnout for the Robotics Education Track, organized by Zachary Dodds, which highlights student-and educator-led robotics projects. In this article we give a summary of these three components of the exhibition.