The International Workshop on Recommendation and Collaboration (ReColl 2008) aims to identify emerging trends in recommendation technology and collaborative environments in the context of intelligent user interfaces. We explore these two topics separately and the synergies between them.

User-based collaborative filtering methods typically predict a user's item ratings as a weighted average of the ratings given by similar users, where the weight is proportional to the user similarity. Therefore, the accuracy of user similarity is the key to the success of the recommendation, both for selecting neighborhoods and computing predictions. However, the computed similarities between users are somewhat inaccurate due to data sparsity.

The learning algorithms used in Programming-by-Demonstration (PBD) are either on-line and incremental or off-line and batch. Neither category is entirely suitable for capturing know-how from demonstrations in a distributed, collaborative environment, where multiple experts can independently provide examples to improve the model.In this paper we describe Distributed Augmentation-Based Learning (DABL), the first real-time PBD learning algorithm suited for distributed know-how acquisition. DABL is an incremental learning algorithm that uses a version-control-like paradigm to combine independently constructed procedure models. An expert can check out a procedure model from a repository and modify it by means of new demonstrations or by manually editing it. The expert then reconciles the changes with those concurrently made by other experts and checked into the repository.DABL automatically merges the two procedures, learns new decision points based on reconcilable differences, and identifies conflicts where there are multiple valid ways of combining the changes or where the combination produces an invalid model, that is, one that does not lie in the search space of the learning algorithm.

While using how-to documents for guidance in performing computer-based tasks, users often run into problems due to inaccurate, out-of-date and incomplete documentation. These problems are often due to current documentation practices, which fail to keep how-to documents current, accurate, and complete. We believe that automated support for incremental update of how-to-documents, through the use of programming by demonstration and guided walkthrough techniques, is more effective than existing practice and produces documents that cause fewer problems for their users. In this paper, we present a study that evaluates this belief by comparing DocWizards, a tool utilizing these techniques, with a standard word processor. We show that more effective and efficient documentation can be generated by multiple authors using DocWizards in an incremental process, with effort comparable to that incurred using a traditional tool.

Much existing documentation is informal and serves to communicate "how-to" knowledge among restricted working groups. Using current practices, such documentation is both difficult to maintain and difficult to use properly.In this paper, we propose a documentation system, called DocWizards, that uses programming by demonstration to support low-cost authoring and guided walkthrough techniques to improve document usability.We report a comparative study between the use of DocWizards and traditional techniques for authoring and following documentation. The study participants showed significant gains in efficiency and reduction in error rates when using DocWizards. In addition, they expressed a clear preference for using the DocWizards tool, both for authoring and for following documentation.

In this paper we introduce a new approach to Programming-by-Demonstration in which the user is allowed to explicitly edit the procedure model produced by the learning algorithm while demonstrating the task. We describe a new algorithm, Augmentation-Based Learning, that supports this approach by considering both demonstrations and edits as constraints on the hypothesis space, and resolving con icts in favor of edits.

Traditional documentation for computer-based procedures is difficult to use: readers have trouble navigating long complex instructions, have trouble mapping from the text to display widgets, and waste time performing repetitive procedures. We propose a new class of improved documentation that we call follow-me documentation wizards. Follow-me documentation wizards step a user through a script representation of a procedure by highlighting portions of the text, as well application UI elements. This paper presents algorithms for automatically capturing follow-me documentation wizards by demonstration, through observing experts performing the procedure. We also present our DocWizards implementation on the Eclipse platform. We evaluate our system with an initial user study that showing that most users have a marked preference for this form of guidance over traditional documentation.

No abstract available.

Technical support procedures are typically very complex. Users often have trouble following printed instructions describing how to perform these procedures, and these instructions are difficult for support personnel to author clearly. Our goal is to learn these procedures by demonstration, watching multiple experts performing the same procedure across different operating conditions, and produce an executable procedure that runs interactively on the user's desktop. Most previous programming by demonstration systems have focused on simple programs with regular structure, such as loops with fixed-length bodies. In contrast, our system induces complex procedure structure by aligning multiple execution traces covering different paths through the procedure. This paper presents a solution to this alignment problem using Input/Output Hidden Markov Models. We describe the results of a user study that examines how users follow printed directions. We present Sheepdog, an implemented system for capturing, learning, and playing back technical support procedures on the Windows desktop. Finally, we empirically evalute our system using traces gathered from the user study and show that we are able to achieve 73% accuracy on a network configuration task using a procedure trained by non-experts.

No abstract available.

An emerging approach to multi-device application development requires developers to build an abstract semantic model that is translated into specific implementations for web browsers, PDAs, voice systems and other user interfaces. Specifying abstract semantics can be difficult for designers accustomed to working with concrete screen-oriented layout. We present an approach to model recovery: inferring semantic models from existing applications, enabling developers to use familiar tools but still reap the benefits of multi-device deployment. We describe MORE, a system that converts the visual layout of HTML forms into a semantic model with explicit captions and logical grouping. We evaluate MOREs performance on forms from existing Web applications, and demonstrate that in most cases the difference between the recovered model and a hand-authored model is under 5%

In this paper, we describe a novel content-based retrieval application which permits astrophysicists to search large image sequence archives for solar phenomenon, such as solar flares, based on the spatio-temporal behavior of the solar phenomenon. Specifically, images are preprocessed to identify bright and dark spots based on their relative intensity with respect to their neighboring regions. Temporally persistent objects are then extracted from the collection of spots, and their spatio-temporal behavior represented as intensity and size time series. Users define a query in terms of a model of spatio-temporal behaviors through a Web-based interface. The stored intensity and size time series are searched, and series segments that match the specified specified spatio-temporal behavior are returned. The benchmark results based on 2500 satellite images show that the proposed methodology demonstrated better than 85% accuracy on a solar phenomenon previously identified by astrophysicists.

This paper describes the Onion technique, a special indexing structure for linear optimization queries. Linear optimization queries ask for top-N records subject to the maximization or minimization of linearly weighted sum of record attribute values. Such query appears in many applications employing linear models and is an effective way to summarize representative cases, such as the top-50 ranked colleges. The Onion indexing is based on a geometric property of convex hull, which guarantees that the optimal value can always be found at one or more of its vertices. The Onion indexing makes use of this property to construct convex hulls in layers with outer layers enclosing inner layers geometrically. A data record is indexed by its layer number or equivalently its depth in the layered convex hull. Queries with linear weightings issued at run time are evaluated from the outmost layer inwards. We show experimentally that the Onion indexing achieves orders of magnitude speedup against sequential linear scan when N is small compared to the cardinality of the set. The Onion technique also enables progressive retrieval, which processes and returns ranked results in a progressive manner. Furthermore, the proposed indexing can be extended into a hierarchical organization of data to accommodate both global and local queries.

In this demo, we will show the implementation of a content-based SPatial Image Retrieval Engine (SPIRE) for multimodal unstructured data. This architecture provides a framework for retrieving multi-modal data including image, image sequence, time series and parametric data from large archives. Dramatic speedup (from a factor of 4 to 35) has been achieved for many search operations such as template matching, texture feature extraction. This framework has been applied and validated in solar flares and petroleum exploration in which spatial and spatial-temporal phenomena are located.

In this paper, we describe description schemes (DSs) for image, video, multimedia, home media, and archive content proposed to the MPEG-7 standard. MPEG-7 aims to create a multimedia content description standard in order to facilitate various multimedia searching and filtering applications. During the design process, special care was taken to provide simple but powerful structures that represent generic multimedia data. We use the extensible markup language (XML) to illustrate and exemplify the proposed DSs because of its interoperability and flexibility advantages. The main components of the image, video, and multimedia description schemes are object, feature classification, object hierarchy, entity-relation graph, code downloading, multi-abstraction levels, and modality transcoding. The home media description instantiates the former DSs proposing the 6-W semantic features for objects, and 1-P physical and 6-W semantic object hierarchies. The archive description scheme aims to describe collections of multimedia documents, whereas the former DSs only aim at individual multimedia documents. In the archive description scheme, the content of an archive is represented using multiple hierarchies of clusters, which may be related by entity-relation graphs. The hierarchy is a specific case of entity-relation graph using a containment relation. We explicitly include the hierarchy structure in our DSs because it is a natural way of defining composite objects, a more efficient structure for retrieval, and the representation structure used in MPEG-4. We demonstrate the feasibility and the efficiency of our description schemes by presenting applications that already use the proposed structures or will greatly benefit from their use. These applications are the visual apprentice, the AMOS-search system, a multimedia broadcast news browser, a storytelling system, and an image meta-search engine, MetaSEEk.

The paper presents an interactive approach for guiding the user's select of colormaps in visualization. PRAVDAColor, implemented as a module in the IBM Visualization Data Explorer, provides the user a selection of appropriate colormaps given the data type and spatial frequency, the user's task, and properties of the human perceptual system.

Article Free Access Share on VIEW: an exploratory molecular visualization system with user-definable interaction sequences Authors: Lawrence D. Bergman View Profile , Jane S. Richardson View Profile , David C. Richardson View Profile , Frederick P. Brooks View Profile Authors Info & Claims SIGGRAPH '93: Proceedings of the 20th annual conference on Computer graphics and interactive techniquesSeptember 1993 Pages 117–126https://doi.org/10.1145/166117.166132Published:01 September 1993Publication History 14citation526DownloadsMetricsTotal Citations14Total Downloads526Last 12 Months17Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF