Benchmarking recommender systems

Recommender Systeme unterstützen aus einem Angebot von Diensten und Produkten auf bspw. Verkaufs- oder Unterhaltungsplattformen, diejenigen zu finden, die die persönlichen Präferenzen bestmöglich abdecken. Durch die Nutzung von Kanälen wie dem Web, sozialen Medien oder E-Mail können zur weiteren Personalisierung auch Produktbewertungen und -betrachtungen oder Interaktionen in sozialen Medien genutzt werden. Diese Arbeit beleuchtet, ob das Aggregieren von diesen Omni-Kanal-Daten die Empfehlungen positiv beeinflusst. Dazu wird zunächst ein Benchmark-Konzept für die Evaluierung kollaborativer Recommender Systeme entwickelt, das ein generisches Datenmodell und Aspekte der Datengenerierung und -aggregation umfasst. Anschließend wird das Konzept durch etablierte Implementierungen auf realen Daten eines Online-Händlers angewendet und die Ergebnisse werden ausführlich diskutiert. Dabei werden neben der Genauigkeit der Empfehlungen auch technische und geschäftliche Perspektiven betrachtet.Recommender systems support finding services and products on, for instance, e-commerce or entertainment platforms, which best cover personal preferences. Through the usage of channels as the Web, social media, and email, product reviews and views or social media interactions can be used for further personalization. This thesis considers whether the aggregation of this omni-channel data influences recommendations positively. In this regard, first, a benchmark concept for the evaluation of collaborative recommender systems is developed, which includes a generic data model as well as data generation and data aggregation aspects. Subsequently, the concept is applied to a real-world data set of an online retailer based on established implementations, and the results are discussed elaborately. Besides the recommendation accuracy, this also includes to consider technical and business perspectives

Enhancing Traditional Recommender Systems via Social Communities

Collaborative Filtering (CF) has become the most popular approach for developing Recommender Systems in diverse business applications. Unfortunately, problems such as the cold-start problem (i.e., new users or items enter the system and for those no previous preference information is available) and the gray sheep problem (i.e., cases in which a user profile does not match any other profile in the user community) are widely recognized for hindering recommendation effectiveness of traditional CF methods. To alleviate such problems, substantial research has focused on enhancing CF with social information about users (e.g., social relationships and communities). However, despite the crescent interest in social-based approaches, researches and practitioners face the challenge of developing their own Recommender System architecture for appropriately combining social and collaborative filtering methods to improve recommendation results. In this paper, we address this issue by introducing a flexible architecture to support researchers and practitioners in the task of designing real-world Recommender Systems that exploit social network data. We focus on detailing our proposed architecture modules and their interplay, potential algorithms for extracting and combining relevant social information, and candidate technologies for handling diverse and massive data volumes. Additionally, we provide an empirical analysis demonstrating the effectiveness of the proposed architecture on alleviating the cold-start problem over a concrete experimental case