Multinomial SVM item recommender for repeat-buying scenarios

Abstract

Most of the common recommender systems deal with the task of generating recommendations for assortments in which a product is usually bought only once, like books or DVDs. However, there are plenty of online shops selling consumer goods like drugstore products, where the customer purchases the same product repeatedly. We call such scenarios repeat-buying scenarios (Böhm et al., Studies in classification, data analysis, and knowledge organization, 2001). For our approach we utilized the results of information geometry (Amari and Nagaoka, Methods of information geometry. Translation of mathematical monographs, vol 191, American Mathematical Society, Providence, 2000) and transformed customer data taken from a repeat-buying scenario into a multinomial space. Using the multinomial diffusion kernel from Lafferty and Lebanon (J Mach Learn Res 6:129–163, 2005) we developed the multinomial SVM (Support Vector Machine) item recommender system MN-SVM-IR to calculate personalized item recommendations for a repeat-buying scenario. We evaluated our SVM item recommender system in a tenfold-cross-validation against the state of the art recommender BPR-MF (Bayesian Personalized Ranking Matrix Factorization) developed by Rendle et al. (BPR: Bayesian personalized ranking from implicit feedback, 2009). The evaluation was performed on a real world dataset taken from a larger German online drugstore. It shows that the MN-SVM-IR outperforms the BPR-MF.