Christian Scheel

Combining Self-Organizing Map Algorithms for Robust and Scalable Intrusion Detection

In the field of intrusion detection systems, the aspect of anomaly detection is very important, and consequently there are many approaches that address these security issues. The usage of self-organizing map (SOM) makes a foundation for some of these approaches, which consequently often have problems to cope with the requirements of huge nowadays networks. The proposed approach focuses on improving the usage of SOMs for anomaly detection, by combining the strengths of different SOM algorithms. The performed evaluations have shown the necessity of paying attention to different aspects, coming along with network nodes, to individually choose the best matching SOM for each nodes anomaly detection

Estimating the magic barrier of recommender systems: a user study

Recommender systems are commonly evaluated by trying to predict known, withheld, ratings for a set of users. Measures such as the Root-Mean-Square Error are used to estimate the quality of the recommender algorithms. This process does however not acknowledge the inherent rating inconsistencies of users. In this paper we present the first results from a noise measurement user study for estimating the magic barrier of recommender systems conducted on a commercial movie recommendation community. The magic barrier is the expected squared error of the optimal recommendation algorithm, or, the lowest error we can expect from any recommendation algorithm. Our results show that the barrier can be estimated by collecting the opinions of users on already rated items.

The Reason Why: A Survey of Explanations for Recommender Systems

Recommender Systems refer to those applications that offer contents or items to the users, based on their previous activity. These systems are broadly used in several fields and applications, being common that an user interact with several recommender systems during his daily activities. However, most of these systems are black boxes which users really don’t understand how to work. This lack of transparency often causes the distrust of the users. A suitable solution is to offer explanations to the user about why the system is offering such recommendations. This work deals with the problem of retrieving and evaluating explanations based on hybrid recommenders. These explanations are meant to improve the perceived recommendation quality from the user’s perspective. Along with recommended items, explanations are presented to the user to underline the quality of the recommendation. Hybrid recommenders should express relevance by providing reasons speaking for a recommended item. In this work we present an attribute explanation retrieval approach to provide these reasons and show how to evaluate such approaches. Therefore, we set up an online user study where users were asked to provide movie feedback. For each rated movie we additionally retrieved feedback about the reasons this movie was liked or disliked. With this data, explanation retrieval can be studied in general, but it can also be used to evaluate such explanations.

Semantic preference retrieval for querying knowledge bases

This work deals with the problem of automatically creating semantic queries for knowledge bases from preference feedback. Semantic knowledge bases are a good source for retrieving entities for item recommendation. We show that preference decisions are not only based on entities, but also on their corresponding predicate-object relations. By extracting the weights from trained preference models, the weighted predicate-object relations can be stored to a user model. The objective is to use such prototype entities in a general user model to formulate semantic queries for recommendation retrieval.