Including Item Characteristics in the Probabilistic Latent Semantic Analysis Model for Collaborative Filtering

We propose a new hybrid recommender system that combines some advantages of collaborative and content-based recommender systems. While it uses ratings data of all users, as do collaborative recommender systems, it is also able to recommend new items and provide an explanation of its recommendations, as do content-based systems. Our approach is based on the idea that there are communities of users that find the same characteristics important to like or dislike a product. This model is an extension of the probabilistic latent semantic model for collaborative filtering with ideas based on clusterwise linear regression. On a movie data set, we show that the model is competitive to other recommenders and can be used to explain the recommendations to the users.algorithms;probabilistic latent semantic analysis;hybrid recommender systems;recommender systems

Molecular Genetics and Hormones: New Frontiers in Entrepreneurship Research

Recent studies suggest that entrepreneurship is partly heritable, but are unable to pinpoint the specific genes involved. This thesis presents results from novel research aiming to identify genes associated with entrepreneurship using genetic data on the molecular level. In addition, the relationship between testosterone and entrepreneurship is examined since genes may exert their influence through this hormone. The thesis starts by reviewing candidate gene studies that test a pre-specified set of genes for association, but which often fail to replicate. An example within the setting of entrepreneurship research is provided to illustrate this last point. Next, the genome-wide association study (GWAS) design is presented that scans the entire genome for associations. However, due to multiple testing, GWAS requires very large sample sizes to establish robust associations and we perform a simulation study to estimate the minimum sample size needed for a GWAS on entrepreneurship. The following part reports evidence that entrepreneurship is partly heritable and around half of the heritability is accounted for by actual molecular genetic data. However, a GWAS on entrepreneurship does not identify robustly associated genes and prediction exercises show that it is currently impossible to predict entrepreneurship solely from molecular genetic data. In the final part, we show that, in contrast to earlier findings, testosterone is not associated with entrepreneurship. Taken as a whole, the results suggest that entrepreneurship is likely to be influenced by hundreds if not thousands of genes with a very small effect size each, implying that very large sample sizes will be needed in future research to discover associated genes. Most importantly, this thesis may serve as a practical guide for studying the molecular genetics of other economic variables. In conclusion, this thesis helps to build the foundations for a novel research field that integrates molecular genetics into economics

Including Item Characteristics in the Probabilistic Latent Semantic Analysis Model for Collaborative Filtering

We propose a new hybrid recommender system that combines some advantages of collaborative and content-based recommender systems. While it uses ratings data of all users, as do collaborative recommender systems, it is also able to recommend new items and provide an explanation of its recommendations, as do content-based systems. Our approach is based on the idea that there are communities of users that find the same characteristics important to like or dislike a product. This model is an extension of the probabilistic latent semantic model for collaborative filtering with ideas based on clusterwise linear regression. On a movie data set, we show that the model is competitive to other recommenders and can be used to explain the recommendations to the users

Living Forever: Entrepreneurial Overconfidence at Older Ages

Overconfidence has been proposed as an explanation for excess market entry by entrepreneurs and low returns in entrepreneurial activities. However, establishing that entrepreneurs are more overconfident than non-entrepreneurs requires the use of representative population samples; in addition, econometric endogeneity issues in survey data must be addressed. To overcome these methodological challenges, we use a measure of overconfidence that employs self-reports of life expectancy. These self-reports are compared to actual life spans in a large sample of the US population. We show that entrepreneurs are indeed more overconfident than non-entrepreneurs. By using fixed-effects panel regression—and thus by exploiting the longitudinal nature of our data—we provide evidence that changes in entrepreneurial status are not associated with changes in subjective life expectancy. These two findings in combination offer evidence that overconfident individuals self- select into entrepreneurship

Genome-wide association studies in economics and entrepreneurship research: promises and limitations

The recently developed genome-wide association study (GWAS) design enables the identification of genes specifically associated with economic outcomes such as occupational and other choices. This is a promising new approach for economics research which we aim to apply to the choice for entrepreneurship. However, due to multiple testing issues, very large sample sizes are needed to differentiate between true and false positives. For a GWAS on entrepreneurship, we expect that a sample size of at least 30,000 observations is required

De genetica van ondernemerschap

genetiGenoombreed associatieonderzoek is een moderne onderzoeksmethode die het mogelijk maakt genen te vinden die geassocieerd zijn met allerlei ziekten en menselijke eigenschappen. Een samenwerkingsverband tussen de Erasmus School of Economics en het Erasmus Medisch Centrum probeert deze veelbelovende methode toe te passen op de keuze voor ondernemerschap

The Molecular Genetic Architecture of Self-Employment

Economic variables such as income, education, and occupation are known to affect mortality and morbidity, such as cardiovascular disease, and have also been shown to be partly heritable. However, very little is known about which genes influence economic variables, although these genes may have both a direct and an indirect effect on health. We report results from the first large-scale collaboration that studies the molecular genetic architecture of an economic variable-entrepreneurship-that was operationalized using self-employment, a widely-available proxy. Our results suggest that common SNPs when considered jointly explain about half of the narrow-sense heritability of self-employment estimated in twin data (σg2/σP2= 25%, h2= 55%). However, a meta-analysis of genome-wide association studies across sixteen studies comprising 50,627 participants did not identify genome-wide significant SNPs. 58 SNPs with p<10-5were tested in a replication sample (n = 3,271), but none replicated. Furthermore, a gene-based test shows that none of the genes that were previously suggested in the literature to influence entrepreneurship reveal significant associations. Finally, SNP-based genetic scores that use results from the meta-analysis capture less than 0.2% of the variance in self-employment in an independent sample (p≥0.039). Our results are consistent with a highly polygenic molecular genetic architecture of self-employment, with many genetic variants of small effect. Although self-employment is a multi-faceted, heavily environmentally influenced, and biologically distal trait, our results are similar to those for other genetically complex and biologically more proximate outcomes, such as height, intelligence, personality, and several diseases

Genome-wide Association Studies and the Genetics of Entrepreneurship

We are currently investigating genetic influences on self-employment in an international research consortium using genome-wide association studies (GWAS). By meta-analysing results from numerous independent samples we address identification issues arising from multiple testing. To our knowledge, this is the earliest attempt to apply GWAS to an economic outcome of a relatively general nature. Our study will reveal potentials and limitations of this approach for economic research.entrepreneurship;genetics

Molecular genetics and economics

The costs of comprehensively genotyping human subjects have fallen to the point where major funding bodies, even in the social sciences, are beginning to incorporate genetic and biological markers into major social surveys. How, if at all, should economists use and combine molecular genetic and economic data from these surveys? What challenges arise when analyzing genetically informative data? To illustrate, we present results from a "genome-wide association study" of educational attainment. We use a sample of 7,500 individuals from the Framingham Heart Study; our dataset contains over 360,000 genetic markers per person. We get some initially promising results linking genetic markers to educational attainment, but these fail to replicate in a second large sample of 9,500 people from the Rotterdam Study. Unfortunately such failure is typical in molecular genetic studies of this type, so the example is also cautionary. We discuss a number of methodological challenges that face researchers who use molecular genetics to reliably identify genetic associates of economic traits. Our overall assessment is cautiously optimistic: this new data source has potential in economics. But researchers and consumers of the genoeconomic literature should be wary of the pitfalls, most notably the difficulty of doing reliable inference when faced with multiple hypothesis problems on a scale never before encountered in social science

Serum testosterone levels in males are not associated with entrepreneurial behavior in two independent observational studies

Previous research has suggested a positive association between testosterone (T) and entrepreneurial behavior in males. However, this evidence was found in a study with a small sample size and has not been replicated. In the present study, we aimed to verify this association using two large, independent, population-based samples of males. We tested the association of T with entrepreneurial behavior, operationalized as self-employment, using data from the Rotterdam Study (N = 587) and the Study of Health in Pomerania (N = 1697). Total testosterone (TT) and sex hormone-binding globulin (SHBG) were measured in the serum. Free testosterone (FT), non-SHBG-bound T (non-SHBG-T), and the TT/SHBG ratio were calculated and used as measures of bioactive serum T, in addition to TT adjusted for SHBG. Using logistic regression models, we found no significant associations between any of the serum T measures and self-employment in either of the samples. To our knowledge, this is the first large-scale study on the relationship between serum T and entrepreneurial behavior