Personalized Recommendation of Related Content Based on Automatic Metadata Extraction

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.

@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},
 }