CiteULike: brusilovsky's dlpaws

Use and reuse of shared lists as a social content type

Werner Geyer — 2008-06-26T18:27:47-00:00

(2008), pp. 1545-1554.

Social networking sites support a variety of shared content types such as photos, videos, or music. More structured or form-based social content types are not mainstream but we have started seeing sites evolve that support them. This paper describes the design and use of structured lists in an enterprise social networking system. As a major feature of our shared lists, we introduced the ability to reuse someone else's list. We report the results on the use and reuse of shared lists based on three months of usage data from 285 users and interviews with 9 users. Our findings suggest that despite the structured nature of lists, our users socialize more around lists than photos, and use lists as a medium for self-representation.

FacetZoom: a continuous multi-scale widget for navigating hierarchical metadata

Raimund Dachselt — 2008-04-18T05:56:35-00:00

(2008), pp. 1353-1356.

Faceted browsing is a promising way to incrementally refine data sets. Current approaches do not scale well in terms of screen size and have shortcomings in interacting with hierarchical facets. This paper introduces FacetZoom, a novel multi-scale widget combining facet browsing with zoomable user interfaces. Hierarchical facets are displayed as space-filling widgets which allow a fast traversal across all levels while simultaneously maintaining context. We contribute both a seamless continuous navigation and a quick tap-and-center interaction. Two prototypes are described which successfully apply the space-structuring widget to continuous, sampled data and an information collection. A formative user study of the latter indicates that the interface scales well to small screens. FacetZoom is versatile and offers consistent searching and browsing behaviors in a multitude of applications and device settings.

PeerChooser: visual interactive recommendation

John O'Donovan — 2008-06-26T16:11:47-00:00

(2008), pp. 1085-1088.

Collaborative filtering (CF) has been successfully deployed over the years to compute predictions on items based on a user's correlation with a set of peers. The black-box nature of most CF applications leave the user wondering how the system arrived at its recommendation. This note introduces PeerChooser, a collaborative recommender system with an interactive graphical explanation interface. Users are provided with a visual explanation of the CF process and opportunity to manipulate their neighborhood at varying levels of granularity to reflect aspects of their current requirements. In this manner we overcome the problem of redundant profile information in CF systems, in addition to providing an explanation interface. Our layout algorithm produces an exact, noiseless graph representation of the underlying correlations between users. PeerChooser's prediction component uses this graph directly to yield the same results as the benchmark. User's then improve on these predictions by tweaking the graph to their current requirements. We present a user-survey in which PeerChooser compares favorably against a benchmark CF algorithm.

Effects of structure and interaction style on distinct search tasks

Robert Capra — 2007-07-15T17:13:13-00:00

(2007), pp. 442-451.

Recommending related papers based on digital library access records

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

(2007), pp. 417-418.

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.

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.

This paper examines the current state of AEH (adaptive educational hypermedia) research into explicit learning style modelling for user personalisation. It addresses the problem of non-naïve test subjects, who are often in user trials, thus contributing to experimental bias. Instead, the authors suggest using real people, i.e. users with a range of backgrounds and abilities, in order to gain a truer insight into evidence-based research. We report on a study carried out with No statistically significant differences were found between experimental groups, learning style preferences or learning environments. We discuss the significance of this, and then critically analyse the use of learning styles in relation to this study and also in the wider context.real users: around 80 children at a UK primary school. The study investigated sequential and global learning styles as a personalisation mechanism in an AEH system. The user trial involved matching and mismatching users and learning environments to see if learning improved. The AEH system used by the children was DEUS, a new e-learning platform that is conceptually similar to WHURLE, an AEH that also used learning styles as its user model. No statistically significant differences were found between experimental groups, learning style preferences or learning environments. We discuss the significance of this, and then critically analyse the use of learning styles in relation to this study and also in the wider context.

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.

Generating semantically enriched user profiles for Web personalization

Sarabjot Anand — 2007-11-06T01:36:21-00:00

ACM Trans. Inter. Tech., Vol. 7, No. 4. (October 2007)

Traditional collaborative filtering generates recommendations for the active user based solely on ratings of items by other users. However, most businesses today have item ontologies that provide a useful source of content descriptors that can be used to enhance the quality of recommendations generated. In this article, we present a novel approach to integrating user rating vectors with an item ontology to generate recommendations. The approach is novel in measuring similarity between users in that it first derives factors, referred to as impacts, driving the observed user behavior and then uses these factors within the similarity computation. In doing so, a more comprehensive user model is learned that is sensitive to the context of the user visit. An evaluation of our recommendation algorithm was carried out using data from an online retailer of movies with over 94,000 movies, 44,000 actors, and 10,000 directors within the item knowledge base. The evaluation showed a statistically significant improvement in the prediction accuracy over traditional collaborative filtering. Additionally, the algorithm was shown to generate recommendations for visitors that belong to sparse sections of the user space, areas where traditional collaborative filtering would generally fail to generate accurate recommendations.