Riesen, Kaspar

Profilbild
E-Mail-Adresse
Geburtsdatum
Projekt
Organisationseinheiten
Berufsbeschreibung
Nachname
Riesen
Vorname
Kaspar
Name
Riesen, Kaspar

Filter

20152
Filter zurücksetzen

Einstellungen

Autor:in: Witschel, Hans Friedrich × Dateien dazu vorhanden?: Ja ×

Suchergebnisse

Gerade angezeigt 1 - 2 von 2
  • Vorschaubild
    Publikation
    A Graph-Based Recommender for Enhancing the Assortment of Web Shops
    (2015) Riesen, Kaspar; Witschel, Hans Friedrich; Galliè, Emidio [in: Proceedings of Workshop on Data Mining in Marketing DMM'2015]
    In this work, we consider a situation where multiple Providers (competitors) serve a common market, using a common infrastructure of sales channels. More speci cally, we focus on multiple web shops that are run by the same web shop platform provider. Our goal is to recommend new items to complement the assortment of a provider, based on user behaviour in the other shops of the same platform. For this new problem, we propose to capture information on how items sell together in a shared product co-occurrence graph. We then adapt known graph-based recommenders to the problem. Further criteria for ranking recommended items are derived as part of a case study conducted in the context of IT web shops. They are combined with the scores of the graph recommenders in a nal ranking function. We evaluate this function with data from our case study context and based on judgments of one shop owner. Our results show that a good ranking can be achieved, reflecting the needs of the shop owner.
    04B - Beitrag Konferenzschrift
  • Vorschaubild
    Publikation
    How to Support Customer Segmentation with Useful Cluster Descriptions
    (2015) Witschel, Hans Friedrich; Riesen, Kaspar; Loo, Simon [in: Proc. of Industrial Conference on Data Mining ICDM'15, 2015]
    Customer or market segmentation is an important Instrument for the optimisation of marketing strategies and product portfolios. Clustering is a popular data mining technique used to support such segmentation { it groups customers into segments that share certain demographic or behavioural characteristics. In this research, we explore several automatic approaches which support an important task that starts after the actual clustering, namely capturing and labeling the "essence" of segments. We conducted an empirical study by implementing several of these approaches, applying them to a data set of customer representations and studying the way our study participants interacted with the resulting cluster representations. Major goal of the present paper is to nd out which approaches exhibit the greatest ease of understanding on the one hand and which of them lead to the most correct interpretation of cluster essence on the other hand. Our results indicate that using a learned decision tree model as a cluster representation provides both good ease of understanding and correctness of drawn conclusions.
    04B - Beitrag Konferenzschrift