In this article we review the existing techniques in group recommender systems and we propose some improvement based on the study of the different individual behaviors when carrying out a decision-making process. Our method includes an analysis of group personality composition and trust between each group member to improve the accuracy of group recommenders. This way we simulate the argumentation process followed by groups of people when agreeing on a common activity in a more realistic way. Moreover, we reflect how they expect the system to behave in a long term recommendation process. This is achieved by including a memory of past recommendations that increases the satisfaction of users whose preferences have not been taken into account in previous recommendations.

In this article we introduce a novel method of making recommendations to groups based on existing techniques of collaborative filtering and taking into account the group personality composition. We have tested our method in the movie recommendation domain and we have experimentally evaluated its behavior under heterogeneous groups according to the group personality composition.

The venture capital (VC) industry offers opportunities for investment in early-stage companies where uncertainty is very high. Unfortunately, the tools investors currently have available are not robust enough to reduce risk and help them managing uncertainty better. Machine learning data-driven approaches can bridge this gap, as they already do in the hedge fund industry. These approaches are now possible because data from thousands of companies over the world is available through platforms such as Crunchbase. Previous academic efforts have focused only on predicting two classes of exits, i.e., being acquired by other company or offering shares to the public, using only one or a few subsets of explanatory variables. These events are typically related to high returns, but also higher risk, making hard for a venture fund to get repeatable and sustainable returns. On the contrary, we will try to predict more possible outcomes including a subsequent funding round or the closure of the company using a large set of signals. In this way, our approach would provide VC investors with more information to set up a portfolio with lower risk that may eventually achieve higher returns than those based on finding unicorns (i.e., companies with a valuation higher than one billion dollars). We will analyze the performance of several machine learning methods in a dataset of over 120,000 early-stage companies in a realistic setting that tries to predict their progress in a 3-year time window. Results show that machine learning can support venture investors in their decision-making processes to find opportunities and better assessing the risk of potential investments.INDEX TERMS Crunchbase, decision support systems, investment, machine learning, risk assessment, venture capital, explainable artificial intelligence.

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