We include the papers on this page to ensure timely dissemination on a noncommercial basis. Copyright and all rights therein are maintained by the authors or by other copyright holders, notwithstanding that they have offered their works here electronically. It is understood that all persons copying this information will adhere to the terms and constraints invoked by the copyrights. These works may not be reposted without the explicit permission of the copyright holder.
2009
Tobias Bannwart, Amancio Bouza, Gerald Reif, Abraham Bernstein, Private Cross-page Movie Recommendations with the Firefox add-on OMORE, 8th International Semantic Web Conference (ISWC 2009), October 2009. (inproceedings/Semantic Web Challenge)
Online stores and Web portals bring information about a myriad of items such as books, CDs, restaurants or movies at the user's fingertips. Although, the Web reduces the barrier to the information, the user is overwhelmed by the number of available items. Therefore, recommender systems aim to guide the user to relevant items. Current recommender systems store user ratings on the server side. This way the scope of the recommendations is limited to this server only. In addition, the user entrusts the operator of the server with valuable information about his preferences.
Thus, we introduce the private, personal movie recommender OMORE, which learns the user model based on the user's movie ratings. To preserve privacy, OMORE is implemented as Firefox add-on which stores the user ratings and the learned user model locally at the client side. Although OMORE uses the features from the movie pages on the IMDb site, it is not restricted to IMDb only. To enable cross-referencing between various movie sites such as IMDb, Amazon.com, Blockbuster, Netflix, Jinni, or Rotten Tomatoes we introduce the movie cross-reference database LiMo which contributes to the Linked Data cloud.
Amancio Bouza, Gerald Reif, Abraham Bernstein, Probabilistic Partial User Model Similarity for Collaborative Filtering, Proceedings of the 1st International Workshop on Inductive Reasoning and Machine Learning on the Semantic Web (IRMLeS2009) at the 6th European Semantic Web Conference (ESWC2009), June 2009. (inproceedings)
Recommender systems play an important role in supporting people getting items they like. One type of recommender systems is user-based collaborative filtering. The fundamental assumption of user-based collaborative filtering is that people who share similar preferences for common items behave similar in the future. The similarity of user preferences is computed globally on common rated items such that partial preference similarities might be missed. Consequently, valuable ratings of partially similar users are ignored. Furthermore, two users may even have similar preferences but the set of common rated items is too small to infer preference similarity. We propose first, an approach that computes user preference similarities based on learned user preference models and second, we propose a method to compute partial user preference similarities based on partial user model similarities. For users with few common rated items, we show that user similarity based on preferences significantly outperforms user similarity based on common rated items.
2008
Amancio Bouza, Gerald Reif, Abraham Bernstein, Harald C. Gall, SemTree: Ontology-Based Decision Tree Algorithm for Recommender Systems, In Proceedings of the 7th International Semantic Web Conference, October 2008. (inproceedings/Poster)
Recommender systems play an important role in supporting people when choosing items from an overwhelming huge number of choices. So far, no recommender system makes use of domain knowledge. We are modeling user preferences with a machine learning approach to recommend people items by predicting the item ratings. Specifically, we propose SemTree, an ontology-based decision tree learner, that uses a reasoner and an ontology to semantically generalize item features to improve the effectiveness of the decision tree built. We show that SemTree outperforms comparable approaches in recommending more accurate recommendations considering domain knowledge.
2007
Amancio Bouza, Implementation of a Graph-Based Knowledge-Browser for a CMS 2007. (diplomathesis)
The success of knowledge transfer is crucial in the area of knowledge management. Not only
companies in outsourcing-relations have the need of successful knowledge transfer. Organizations
have the need of successful knowledge transfer too in order to create market advantages. This thesis
introduces a graph-based knowledge browser for a CMS to support the topic of knowledge transfer by
providing ?shared material? for generating knowledge and providing easy access to knowledge by
visualizing knowledge as associative networks. Knowledge is presented as graph or radial layout in
hyperspace. Web 2.0 technologies like AJAX and SVG are used for the implementation.
1st International Workshop on Inductive Reasoning and Machine Learning for the Semantic Web (IRMLeS2009) at the 6th European Semantic Web Conference (ESWC2009):
, Private Cross-page Movie Recommendations with the Firefox add-on OMORE, 8th International Semantic Web Conference (ISWC 2009), October 2009. (inproceedings/Semantic Web Challenge)
