Lukas Lerche

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In academic studies, the evaluation of recommender system (RS) algorithms is often limited to offline experimental designs based on historical data sets and metrics from the fields of Machine Learning or Information Retrieval. In real-world settings, however, other business-oriented metrics such as click-through-rates, customer retention or effects on the(More)
RapidMiner is a software framework for the development and execution of data analysis workflows. Like many modern software development environments, the tool comprises a visual editor which allows the user to design processes on a conceptual level, thereby abstracts technical details, and thus helps the user focus on the core modeling task. The large set of(More)
Automated playlist generation is a special form of music recommendation and a common feature of digital music playing applications. A particular challenge of the task is that the recommended items should not only match the general listener's preference but should also be coherent with the most recently played tracks. In this work, we propose a novel(More)
In many application domains of recommender systems, explicit rating information is sparse or non-existent. The preferences of the current user have therefore to be approximated by interpreting his or her behavior, i.e., the implicit user feedback. In the literature, a number of algorithm proposals have been made that rely solely on such implicit feedback,(More)
An essential characteristic in many e-commerce settings is that website visitors can have very specific short-term shopping goals when they browse the site. Relying solely on long-term user models that are pre-trained on historical data can therefore be insufficient for a suitable next-basket recommendation. Simple "real-time" recommendation approaches(More)
Most real-world recommender systems are deployed in a commercial context or designed to represent a value-adding service, e.g., on shopping or Social Web platforms, and typical success indicators for such systems include conversion rates, customer loyalty or sales numbers. In academic research, in contrast, the evaluation and comparison of different(More)
User studies play an important role in academic research in the field of recommender systems as they allow us to assess quality factors other than the predictive accuracy of the underlying algorithms. User satisfaction is one such factor that is often evaluated in laboratory settings and in many experimental designs one task of the participants is to assess(More)
Machine learning and data analytics tasks in practice require several consecutive processing steps. RapidMiner is a widely used software tool for the development and execution of such analytics workflows. Unlike many other algorithm toolkits, it comprises a visual editor that allows the user to design processes on a conceptual level. This conceptual and(More)
Most research in recommender systems is focused on the problem of identifying and ranking items that are relevant for the individual users but unknown to them. The potential value of such systems is to help users discover new items, e.g., in e-commerce settings. Many real-world systems however also utilize recommendation lists for a different goal, namely(More)
The automated generation of playlists given a user's most recent listening history is a common feature of modern music streaming platforms. In the research literature, a number of algorithmic proposals for this " next-track recommendation " problem have been made in recent years. However, nearly all of them are based on the user's most recent listening(More)