CiteULike: Group: Adaptive-Web - library [448 articles]

Mining User preference using Spy voting for search engine personalization

Wilfred Ng — 2008-05-06T14:43:39-00:00

ACM Trans. Interet Technol., Vol. 7, No. 4. (October 2007)

Time-dependent event hierarchy construction

Gabriel — 2007-11-02T01:59:42-00:00

(2007), pp. 300-309.

Recommending related papers based on digital library access records

Stefan Pohl — 2008-04-17T15:53:00-00:00

(2007), pp. 417-418.

The challenge of information visualization evaluation

Catherine Plaisant — 2005-02-22T22:19:19-00:00

(2004), pp. 109-116.

Using Social Annotations to Smooth the Language Model for IR

Shengliang Xu — 2007-10-04T06:20:21-00:00

Advances in Knowledge Discovery and Data Mining (2007), pp. 1015-1021.

In the paper, we present an exploration of using social annotations provided by the Web 2.0 sites (such as Del.icio.us) in helping web search. More specifically, we consider using the social annotations as an additional resource to strengthen existing smoothing methods for the language model for IR. The social annotations can benefit the smoothing of language model in two aspects: 1) the annotations themselves can serve as the summaries of the web pages given by the users; 2) the annotations can be seen as the links of the web pages sharing the same annotations. We propose three smoothing methods, addressing the two aspects and their combination, respectively. We call the new language model of using the proposed smoothing methods ’Language Annotation Model (LAM). Preliminary experimental results show that LAM significantly outperforms the traditional language models.

Electronic peer review: A large cohort teaching themselves?

2008-04-02T15:30:24-00:00

Do students SQLify?: improving learning outcomes with peer review and enhanced computer assisted assessment of querying skills - USQ ePrints

2008-04-02T15:23:48-00:00

Evaluating the accuracy of implicit feedback from clicks and query reformulations in Web search

Thorsten Joachims — 2008-01-04T21:39:32-00:00

ACM Trans. Inf. Syst., Vol. 25, No. 2. (April 2007)

Learning user interaction models for predicting web search result preferences

Eugene Agichtein — 2006-12-19T18:38:47-00:00

(2006), pp. 3-10.

Social information filtering: algorithms for automating “word of mouth”

Upendra Shardanand — 2005-08-10T18:07:44-00:00

(1995), pp. 210-217.

Content-based music filtering system with editable user profile

Yoshinori Hijikata — 2008-03-30T02:56:10-00:00

(2006), pp. 1050-1057.

A Location Aware Mobile Tourist Guide Selecting and Interpreting Sights and Services by Context Matching

Klaus Hagen — 2008-03-24T21:29:50-00:00

(2005), pp. 293-304.

Augmenting audio messages with visual directions in mobile guides: an evaluation of three approaches

Luca Chittaro — 2008-03-24T21:25:44-00:00

(2005), pp. 107-114.

Visualizing locations of off-screen objects on mobile devices: a comparative evaluation of three approaches

Stefano Burigat — 2008-03-24T21:21:20-00:00

(2006), pp. 239-246.

Linked: How Everything Is Connected to Everything Else and What It Means for Business, Science, and Everyday Life

Albert-Laszlo Barabasi — 2005-02-27T02:19:34-00:00

(01 April 2003)

A cocktail party. A terrorist cell. Ancient bacteria. An international conglomerate. <br><br> All are networks, and all are a part of a surprising scientific revolution. Albert-L&aacuteszl&oacute Barab&aacutesi, the nation's foremost expert in the new science of networks, takes us on an intellectual adventure to prove that social networks, corporations, and living organisms are more similar than previously thought. Grasping a full understanding of network science will someday allow us to design blue-chip businesses, stop the outbreak of deadly diseases, and influence the exchange of ideas and information. Just as James Gleick brought the discovery of chaos theory to the general public, Linked tells the story of the true science of the future.

Learning user profiles for personalized information dissemination

Ah-Hwee Tan — 2008-03-15T21:54:29-00:00

Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on, Vol. 1 (1998), pp. 183-188 vol.1.

Personalized information systems represent the recent effort of delivering information to users more effectively in the modern electronic age. This paper illustrates how a supervised adaptive resonance theory (ART) system, called fuzzy ARAM (adaptive resonance associative map), can be used to learn user profiles for personalized information dissemination. ARAM learning is online, fast, and incremental. Acquisition of new knowledge does not require re-training on previously learned cases. ARAM integrates both user-defined and system-learned knowledge in a single framework. Therefore inconsistency between the two knowledge sources will not arise. ARAM has been used to develop a personalized news system (PIN). Preliminary experiments have verified that PIN is able to provide personalized news by adapting to user's interests in an online manner and generalizing them to new information on-the-fly

Moving digital libraries into the student learning space: The GetSmart experience

Byron Marshall — 2008-03-13T14:29:45-00:00

J. Educ. Resour. Comput., Vol. 6, No. 1. (March 2006)

Personalizing Image Search Results on Flickr

Kristina Lerman — 2007-04-16T16:57:49-00:00

(12 Apr 2007)

The social media site Flickr allows users to upload their photos, annotate them with tags, submit them to groups, and also to form social networks by adding other users as contacts. Flickr offers multiple ways of browsing or searching it. One option is tag search, which returns all images tagged with a specific keyword. If the keyword is ambiguous, e.g., “beetle” could mean an insect or a car, tag search results will include many images that are not relevant to the sense the user had in mind when executing the query. We claim that users express their photography interests through the metadata they add in the form of contacts and image annotations. We show how to exploit this metadata to personalize search results for the user, thereby improving search performance. First, we show that we can significantly improve search precision by filtering tag search results by user's contacts or a larger social network that includes those contact's contacts. Secondly, we describe a probabilistic model that takes advantage of tag information to discover latent topics contained in the search results. The users' interests can similarly be described by the tags they used for annotating their images. The latent topics found by the model are then used to personalize search results by finding images on topics that are of interest to the user.

Enhancing Case-Based, Collaborative Web Search

Oisín Boydell — 2008-02-17T03:33:02-00:00

Case-Based Reasoning Research and Development (2007), pp. 329-343.

This paper describes and evaluates a case-based approach to personalizing Web search by post-processing the results returned by a Web search engine to reflect the interests of a community of like-minded searchers. The search experiences of a community of users are captured as a case base of textual cases, which serves as a way to bias future search results in line with community interests.

Relevance feedback from eye movements for proactive information retrieval

S Arvi — 2008-02-06T18:51:30-00:00

(2004)

Introduction. This on-going work is part of a larger project that aims at complementing or even replacing the laborious explicit relevance feedback in information retrieval by implicit feedback. Technology for measuring eye movements begins to be mature enough, and the movements contain rich information about the attention and interest patterns of the user. The problem is that the signal is very noisy and the correspondence of the eye xation patterns to user's attention is not always...

INTRIGUE: Personalized recommendation of tourist attractions for desktop and handset devices

Liliana Ardissono — 2008-01-23T20:56:08-00:00

Applied Artificial Intelligence, Vol. 17, No. 8. (2003), pp. 687-714.

This paper presents INTRIGUE, a prototype tourist-information server that presents information about the area around Turin City, Italy, on desktop and hand held devices.This system recommends sightseeing destinations and itineraries by taking into account the preferences of heterogeneous tourist groups (such as families with children or the elderly) and explains the recommendations by addressing the group members' requirements. Moreover, the system provides an interactive agenda for scheduling the tour. The services offered by INTRIGUE rely on user modeling and adaptive hypermedia techniques; furthermore, XML-based technologies support the generation of the user interface and its adaptation to Web browsers and WAP minibrowsers.

Information retrieval based on collaborative filtering with latent interest semantic map

Noriaki Kawamae — 2008-01-23T20:13:05-00:00

(2005), pp. 618-623.

Relevant term suggestion in interactive web search based on contextual information in query session logs

Chien-Kang Huang — 2004-11-29T16:56:04-00:00

J. Am. Soc. Inf. Sci. Technol., Vol. 54, No. 7. (May 2003), pp. 638-649.

User modeling for full-text federated search in peer-to-peer networks

Jie Lu — 2008-01-23T20:02:07-00:00

(2006), pp. 332-339.

Improving Semantic Search Via Integrated Personalized Faceted and Visual Graph Navigation

Michal Tvarožek — 2008-01-23T19:38:13-00:00

SOFSEM 2008: Theory and Practice of Computer Science (2008), pp. 778-789.

Growing need for information retrieval, information processing, and the associated need for navigation in existing information spaces resulted in several approaches that aim to improve efficiency of the respective user tasks. However, problems related to user navigation and orientation in large open information spaces still persist possibly due to increasing demands and the imperfections of individual approaches. We propose an integrated search and navigation solution that takes advantage of the faceted browsing paradigm and visual navigation in graphs both extended with support for automatic personalization based on user context also taking advantage of a user’s social network. The proposed solution is primarily evaluated in the domain of scientific publications, i.e. digital libraries, with possible extensions to other application domains.

Best of Both: Using Semantic Web Technologies to Enrich User Interaction with the Web and Vice Versa

Martin Dzbor — 2008-01-23T19:27:50-00:00

SOFSEM 2008: Theory and Practice of Computer Science (2008), pp. 34-49.

Human-computer interaction is a well-established and rich subject that has an impact not only on those who develop computational systems, but also on the users of such systems, the vendors, maintainers, and many more stakeholders who are normally involved in designing and delivering software and computer-based tools. Interaction in this context is seen broadly – in general, it involves three constituting parts: the user, the technology, and the way they work together. One can then study such phenomena as how the users work with a particular technology, what the users prefer, how the technology addresses given issues, etc. In this contribution I want to look at a specific type of user interaction – with semantically enriched content in general, and with ontologies in particular.

Social Information Access: The Other Side of the Social Web

Peter Brusilovsky — 2008-01-23T19:16:18-00:00

SOFSEM 2008: Theory and Practice of Computer Science (2008), pp. 5-22.

Modern Web, which is frequently called Social Web or Web 2.0, celebrates the power of the user community. Most frequently it is associated with the power of users as contributors or various kinds of contents through Wikis, blogs, and resource sharing sites. However, the community power impacts not only the production of Web content, but also the access to all kinds of Web content. A number of research groups worldwide work on social information access techniques, which help users get to the right information using “community wisdom” distilled from tracked actions of those who worked with this information earlier. The paper provides an overview of this research stream focusing on social search, social navigation, and social visualization techniques.

Social Rewarding in Wiki Systems – Motivating the Community

Bernhard Hoisl — 2007-10-30T16:58:04-00:00

Online Communities and Social Computing (2007), pp. 362-371.

Online communities have something in common: their success rise and fall with the participation rate of active users. In this paper we focus on social rewarding mechanisms that generate benefits for users in order to achieve a higher contribution rate in a wiki system. In an online community, social rewarding is in the majority of cases based on accentuation of the most active members. As money cannot be used as a motivating factor others like status, power, acceptance, and glory have to be employed. We explain different social rewarding mechanisms which aim to meet these needs of users. Furthermore, we implemented a number of methods within the MediaWiki system, where social rewarding criteria are satisfied by generating a ranking of most active members.

Contextual relevance feedback in web information retrieval

Dilip Limbu — 2007-11-26T20:07:02-00:00

(2006), pp. 138-143.

On the Use of Self-Organizing Maps for Clustering and Visualization

Arthur Flexer — 2008-01-09T07:06:50-00:00

Principles of Data Mining and Knowledge Discovery (1999), pp. 80-88.

We show that the number of output units used in a self-organizing map (SOM) influences its applicability for either clustering or visualization. By reviewing the appropriate literature and theory and own empirical results, we demonstrate that SOMs can be used for clustering or visualization separately, for simultaneous clustering and visualization, and even for clustering via visualization. For all these different kinds of application, SOM is compared to other statistical approaches. This will show SOM to be a flexible tool which can be used for various forms of explorative data analysis but it will also be made obvious that this flexibility comes with a price in terms of impaired performance. The usage of SOM in the data mining community is covered by discussing its application in the data mining tools CLEMENTINE and WEBSOM.

Clustering of the self-organizing map

J Vesanto — 2008-01-09T06:38:04-00:00

Neural Networks, IEEE Transactions on, Vol. 11, No. 3. (2000), pp. 586-600.

The self-organizing map (SOM) is an excellent tool in exploratory phase of data mining. It projects input space on prototypes of a low-dimensional regular grid that can be effectively utilized to visualize and explore properties of the data. When the number of SOM units is large, to facilitate quantitative analysis of the map and the data, similar units need to be grouped, i.e., clustered. In this paper, different approaches to clustering of the SOM are considered. In particular, the use of hierarchical agglomerative clustering and partitive clustering using K-means are investigated. The two-stage procedure-first using SOM to produce the prototypes that are then clustered in the second stage-is found to perform well when compared with direct clustering of the data and to reduce the computation time

The Wisdom of Crowds: Why the Many Are Smarter Than the Few and How Collective Wisdom Shapes Business, Economies, Societies and Nations

James Surowiecki — 2005-04-12T02:21:51-00:00

(25 May 2004)

Collaborative Filtering Recommender Systems

J Schafer — 2007-09-18T11:50:41-00:00

The Adaptive Web (2007), pp. 291-324.

One of the potent personalization technologies powering the adaptive web is collaborative filtering. Collaborative filtering (CF) is the process of filtering or evaluating items through the opinions of other people. CF technology brings together the opinions of large interconnected communities on the web, supporting filtering of substantial quantities of data. In this chapter we introduce the core concepts of collaborative filtering, its primary uses for users of the adaptive web, the theory and practice of CF algorithms, and design decisions regarding rating systems and acquisition of ratings. We also discuss how to evaluate CF systems, and the evolution of rich interaction interfaces. We close the chapter with discussions of the challenges of privacy particular to a CF recommendation service and important open research questions in the field.

Social bookmarking and exploratory search

David Millen — 2007-10-30T17:03:06-00:00

ECSCW 2007 (2007), pp. 21-40.

In this paper, we explore various search tasks that are supported by a social bookmarking service. These bookmarking services hold great potential to powerfully combine personal tagging of information sources with interactive browsing, resulting in better social navigation. While there has been considerable interest in social tagging systems in recent years, little is known about their actual usage. In this paper, we present the results of a field study of a social bookmarking service that has been deployed in a large enterprise. We present new qualitative and quantitative data on how a corporate social tagging system was used, through both event logs (click level analysis) and interviews. We observed three types of search activities: community browsing, personal search, and explicit search. Community browsing was the most frequently used, and confirms the value of the social aspects of the system. We conclude that social bookmarking services support various kinds of exploratory search, and provide better personal bookmark management and enhance social navigation.

A content-collaborative recommender that exploits WordNet-based user profiles for neighborhood formation

Degemmis — 2007-07-10T06:33:10-00:00

User Modeling and User-Adapted Interaction, Vol. 17, No. 3. (July 2007), pp. 217-255.

Adaptive, intelligent presentation of information for the museum visitor in PEACH

Stock — 2007-07-10T06:33:10-00:00

User Modeling and User-Adapted Interaction, Vol. 17, No. 3. (July 2007), pp. 257-304.

Simplifying web traversals by recognizing behavior patterns

Christian Doerr — 2007-12-21T21:05:21-00:00

(2007), pp. 105-114.

Real users, real results: examining the limitations of learning styles within AEH

Elizabeth Brown — 2007-12-21T17:01:53-00:00

(2007), pp. 57-66.

Mobility agents: guiding and tracking public transportation users

Alexander Repenning — 2007-12-12T21:58:03-00:00

(2006), pp. 127-134.

User Experiments with Tree Visualization Systems

A Kobsa — 2007-12-08T22:15:20-00:00

Information Visualization, 2004. INFOVIS 2004. IEEE Symposium on (2004), pp. 9-16.

This paper describes a comparative experiment with five well-known tree visualization systems, and Windows Explorer as a baseline system. Subjects performed tasks relating to the structure of a directory hierarchy, and to attributes of files and directories. Task completion times, correctness and user satisfaction were measured, and video recordings of subjects' interaction with the systems were made. Significant system and task type effects and an interaction between system and task type were found. Qualitative analyses of the video recordings were thereupon conducted to determine reasons for the observed differences, resulting in several findings and design recommendations as well as implications for future experiments with tree visualization systems

Autonomously semantifying wikipedia

Fei Wu — 2007-12-07T21:42:51-00:00

(2007), pp. 41-50.

Relevance feedback and personalization: A language modeling perspective

WB Ponte — 2007-12-11T20:32:23-00:00

DELOS Workshop: Personalisation and Recommender Systems in Digital Libraries (2001)

Automatic ontology-based knowledge extraction from Web documents

H Alani — 2006-09-18T14:47:09-00:00

Intelligent Systems, IEEE [see also IEEE Intelligent Systems and Their Applications], Vol. 18, No. 1. (2003), pp. 14-21.

To bring the Semantic Web to life and provide advanced knowledge services, we need efficient ways to access and extract knowledge from Web documents. Although Web page annotations could facilitate such knowledge gathering, annotations are rare and will probably never be rich or detailed enough to cover all the knowledge these documents contain. Manual annotation is impractical and unscalable, and automatic annotation tools remain largely undeveloped. Specialized knowledge services therefore require tools that can search and extract specific knowledge directly from unstructured text on the Web, guided by an ontology that details what type of knowledge to harvest. An ontology uses concepts and relations to classify domain knowledge. Other researchers have used ontologies to support knowledge extraction, but few have explored their full potential in this domain. The paper considers the Artequakt project which links a knowledge extraction tool with an ontology to achieve continuous knowledge support and guide information extraction. The extraction tool searches online documents and extracts knowledge that matches the given classification structure. It provides this knowledge in a machine-readable format that will be automatically maintained in a knowledge base (KB). Knowledge extraction is further enhanced using a lexicon-based term expansion mechanism that provides extended ontology terminology.

Incorporating user control into recommender systems based on naive bayesian classification

Verus Pronk — 2007-12-07T20:03:31-00:00

(2007), pp. 73-80.

Supporting social recommendations with activity-balanced clustering

Maxwell Harper — 2007-12-03T16:18:23-00:00

(2007), pp. 165-168.

Eye-tracking for user modeling in exploratory learning environments: An empirical evaluation

Cristina Conati — 2007-11-30T20:02:13-00:00

Knowledge-Based Systems, Vol. 20, No. 6. (August 2007), pp. 557-574.

In this paper, we describe research on using eye-tracking data for on-line assessment of user meta-cognitive behavior during interaction with an environment for exploration-based learning. This work contributes to user modeling and intelligent interfaces research by extending existing research on eye-tracking in HCI to on-line capturing of high-level user mental states for real-time interaction tailoring. We first describe the empirical work we did to understand the user meta-cognitive behaviors to be modeled. We then illustrate the probabilistic user model we designed to capture these behaviors with the help of on-line information on user attention patterns derived from eye-tracking data. Next, we describe the evaluation of this model, showing that gaze-tracking data can significantly improve model performance compared to lower level, time-based evidence. Finally, we discuss work we have done on using pupil dilation information, also gathered through eye-tracking data, to further improve model accuracy.

A Component Architecture for Dynamically Managing Privacy Constraints in Personalized Web-Based Systems

Alfred Kobsa — 2007-11-29T00:57:06-00:00

Privacy Enhancing Technologies (2003), pp. 177-188.

User-adaptive (or ”personalized”) systems on the web cater their interaction to each individual user and provide considerable benefits to both users and web vendors. These systems pose privacy problems, however, since they must collect large amounts of personal information to be able to adapt to users, and often do this in a rather inconspicuous manner. The interaction with personalized systems is therefore likely to be affected by users’ privacy concerns, and is in many cases also subject to privacy laws and self-regulatory privacy principles. An analysis of nearly 30 international privacy laws revealed that many of them impose severe restrictions not only on the data that may be collected but also on the personalization methods that may be employed. For many personalization goals, more than one methods can be used that differ in their data and privacy requirements and their anticipated accuracy and reliability. This paper presents a software architecture that encapsulates the different personalization methods in individual components and, at any point during runtime, ascertains the dynamic selection of the component with the optimal anticipated personalization effects among those that are permissible under the currently prevailing privacy constraints.

Modeling human behavior in user-adaptive systems: Recent advances using soft computing techniques

E Frias-Martinez — 2007-11-28T23:33:26-00:00

Expert Systems with Applications, Vol. 29, No. 2. (August 2005), pp. 320-329.

Adaptive Hypermedia systems are becoming more important in our everyday activities and users are expecting more intelligent services from them. The key element of a generic adaptive hypermedia system is the user model. Traditional machine learning techniques used to create user models are usually too rigid to capture the inherent uncertainty of human behavior. In this context, soft computing techniques can be used to handle and process human uncertainty and to simulate human decision-making. This paper examines how soft computing techniques, including fuzzy logic, neural networks, genetic algorithms, fuzzy clustering and neuro-fuzzy systems, have been used, alone or in combination with other machine learning techniques, for user modeling from 1999 to 2004. For each technique, its main applications, limitations and future directions for user modeling are presented. The paper also presents guidelines that show which soft computing techniques should be used according to the task implemented by the application.

Relevance feedback versus local context analysis as term suggestion devices

N Belkin — 2007-11-27T22:54:27-00:00

(2000)

Query formulation and reformulation is recognized as one of the most difficult tasks that users in information retrieval systems are asked to perform. This study investigated the use of two different techniques for supporting query reformulation in interactive information retrieval: relevance feedback and Local Context Analysis, both implemented as term-suggestion devices. The former represents techniques which offer user control and understanding of term suggestion; the latter represents...

Getting to know you: learning new user preferences in recommender systems

Al Rashid — 2007-05-17T18:34:08-00:00

(2002), pp. 127-134.