A Personalized Approach to Experience-Aware Service Ranking and Selection
A Service Distribution Protocol For Mobile Ad Hoc Networks
A Service Distribution Protocol For Mobile Ad Hoc Networks
Adaptive Portals: Adapting and Recommending Content and Expertise
Adaptive Treemap Based Navigation Through Web Portals
An Extended Analysis of an Interest-Based Service Distribution Protocol for Mobile Ad-Hoc Networks
Book Chapter: Comparison: Handling Preferences with DIANE and miAamics
Book Chapter: Semantic Service Discovery with DIANE Service Descriptions
Book Chapter: Service Discovery with SWE-ET and DIANE – An In-depth Comparison By Means of a Common Scenario
Book Chapter: Status, Perspectives, and Lessons Learned
Book Chapter: SWS Challenge Scenarios
Evaluating Semantic Web Service Matchmaking Effectiveness Based on Graded Relevance
On the Empirical Evaluation of Semantic Web Service Approaches: Towards Common SWS Test Collections
On the Evaluation of Semantic Web Service Frameworks
Ontology-Based Multidimensional Personalization Modeling for the Automatic Generation of Mashups in Next-Generation Portals
OPOSSum – An Online Portal to Collect and Share Semantic Service Descriptions
OPOSSum – An Online Portal to Collect and Share SWS Descriptions (Best Demo Award!)
Personalized Recommendation of Related Content Based on Automatic Metadata Extraction
Proceedings of the 20. Workshop on Foundations of Databases (Grundlagen von Datenbanken)
Recommending Background Information and Related Content in Web Portals using Unstructured Data Analysis
Service Availability, Success Ratio, Prevalence, Replica Allocation Correctness, Replication Degree, and Effects of Different Replication/Hibernation Behavior Effects of the Service Distribution Protocol for Mobile Ad Hoc Networks -A Detailed Study-
Towards an Automatic Service Composition for Generation of User-Sensitive Mashups
Towards Standard Test Collections for the Empirical Evaluation of Semantic Web Service Approaches
Using Context Information to Evaluate Cooperativeness
W3C SWS Challenge Testbed Incubator Methodology Report
Personalized Recommendation of Related Content Based on Automatic Metadata Extraction
Title: | Personalized Recommendation of Related Content Based on Automatic Metadata Extraction |
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Authors: | Andreas Nauerz, Fedor Bakalov, Birgitta König-Ries, Martin Welsch |
Source: | Proceedings of the 18th Annual International Conference on Computer Science and Software Engineering |
Place: | Toronto, Canada |
Date: | 2008-10-01 |
Type: | Conference Paper |
Abstract: |
In order to efficiently use information, users often need access to additional background information. This additional information might be stored at various places, such as news websites, company directories, geographic information systems, etc. Oftentimes, in order to access these different pieces of information, the user has to launch new browser windows and direct them to appropriate resources. In our today’s Web 2.0, the problem of accessing background information becomes even more prominent: Due to the large number of different users contributing, Web 2.0 sites grow quickly and, most often, in a more uncoordinated way regarding, e.g., structure and vocabulary used, than centrally controlled sites. In such an environment, finding relevant information can become a tedious task. In this paper, we propose a framework allowing for automated, user-specific annotation of content in order to enable provisioning of related information. Making use of unstructured data analysis services like UIMA or Calais, we are able to identify certain types of entities like locations, persons, etc. These entities are wrapped into semantic tags that contain machine-readable information about the entity type. The entity types are associated with applications able to provide background information or related content. A location, e.g., could be associated with Google Maps, whereas a person could be associated with the company’s employee directory. However, it strongly depends on the individual user’s interests and experience which additional information he deems relevant. We therefore tailor the information provided based on the User Model, which reflects the user’s interests and expertise. This allows providing the user with in-place, in-context background information on those entities he is likely to be interested in as well as with recommendations to related content for those entities. It also relieves users from the tedious task of manually collecting relevant additional information. The approach is being implemented within IBM’s WebSphere Portal. |
File: | cd-web-version.pdf |
Slides: | Presentation.ppt |
URL: | http://doi.acm.org/10.1145/1463788.1463795 |
BibTex: |
@inproceedings{1463795, author = {Nauerz,, Andreas and Bakalov,, Fedor and K\"{o}nig-Ries,, Birgitta and Welsch,, Martin}, title = {Personalized recommendation of related content based on automatic metadata extraction}, booktitle = {CASCON '08: Proceedings of the 2008 conference of the center for advanced studies on collaborative research}, year = {2008}, pages = {57--71}, location = {Ontario, Canada}, doi = {http://doi.acm.org/10.1145/1463788.1463795}, publisher = {ACM}, address = {New York, NY, USA}, } |