Recommender systems in the domain of early-stage enterprise Investment : evaluation in the context of recommendation systems

Abstract

The main objective of this thesis consists of the design of a recommender system, representing a novel method concerning the computational recommendation of early-stage enterprises to investors. In order to quantify decision rules the recommender system is based on, investors requirements and behaviours need to be analysed utilizing qualitativeand quantitative research. This research is done in a previous study of co-author Christian Ohrfandl. Furthermore, demonstrating the behaviour of the proposed recommendation algorithms is a major task of this thesis. For this reason, a prototype of the recommender system is being crafted in software. The chapter Recommender Systems for Early-Stage Enterprise Investment addresses the conceptualization of a recommendation system in the domain of early-stage enterprise investment based on the findings of the specialization topic Investment Decision-making & Venture Valuation by the co-author Christian Ohrfandl. The resulting recommender system includes various types of recommenders in a parallelized approach, that is, Collaborative Filtering, content-based-, knowledge-based-, socialand hybrid recommendation algorithms. Additionally, the conceptual model of this recommender system has been implemented as a highly scalable, plugin-based software prototype that may be easily extended by different recommendation algorithms in future work. The most important opportunity for future research is stated as qualitativeor quantitative evaluations of recommendation quality in terms of user satisfaction. These evaluations may answer the question, whether the implemented design decisions improve a users utility when using the system. In fact, it is precisely this very evaluation that is being researched in the course of the specialization topic chapter Evaluation.