Context-aware LDA: Balancing Relevance and Diversity in TV Content Recommenders

In the vast and expanding ocean of digital content, users are hardly satisfied with recommended programs solely based on static user patterns and common statistics. Therefore, there is growing interest in recommendation approaches that aim to provide a certain level of diversity, besides precision and ranking. Context-awareness, which is an effective way to express dynamics and adaptivity, is widely used in recom-mender systems to set a proper balance between ranking and diversity. In light of these observations, we introduce a recommender with a context-aware probabilistic graphi-cal model and apply it to a campus-wide TV content de-livery system named “Vision”. Within this recommender, selection criteria of candidate fields and contextual factors are designed and users’ dependencies on their personal pref-erence or the aforementioned contextual influences can be distinguished. Most importantly, as to the role of balanc-ing relevance and diversity, final experiment results prove that context-aware LDA can evidently outperform other al-gorithms on both metrics. Thus this scalable model can be flexibly used for different recommendation purposes

Context-aware LDA: Balancing Relevance and Diversity in TV Content Recommenders

In the vast and expanding ocean of digital content, users are hardly satisfied with recommended programs solely based on static user patterns and common statistics. Therefore, there is growing interest in recommendation approaches that aim to provide a certain level of diversity, besides precision and ranking. Context-awareness, which is an effective way to express dynamics and adaptivity, is widely used in recom-mender systems to set a proper balance between ranking and diversity. In light of these observations, we introduce a recommender with a context-aware probabilistic graphi-cal model and apply it to a campus-wide TV content de-livery system named “Vision”. Within this recommender, selection criteria of candidate fields and contextual factors are designed and users’ dependencies on their personal pref-erence or the aforementioned contextual influences can be distinguished. Most importantly, as to the role of balanc-ing relevance and diversity, final experiment results prove that context-aware LDA can evidently outperform other al-gorithms on both metrics. Thus this scalable model can be flexibly used for different recommendation purposes

Analytical, Theoretical and Empirical Advances in Genome-Scale Algorithmics

Ever-increasing amounts of complex biological data continue to come on line daily. Examples include proteomic, transcriptomic, genomic and metabolomic data generated by a plethora of high-throughput methods. Accordingly, fast and effective data processing techniques are more and more in demand. This issue is addressed in this dissertation through an investigation of various algorithmic alternatives and enhancements to routine and traditional procedures in common use. In the analysis of gene co-expression data, for example, differential measures of entropy and variation are studied as augmentations to mere differential expression. These novel metrics are shown to help elucidate disease-related genes in wide assortments of case/control data. In a more theoretical spirit, limits on the worst-case behavior of density based clustering methods are studied. It is proved, for instance, that the well-known paraclique algorithm, under proper tuning, can be guaranteed never to produce subgraphs with density less than 2/3. Transformational approaches to efficient algorithm design are also considered. Classic graph search problems are mapped to and from well-studied versions of satisfiability and integer linear programming. In so doing, regions of the input space are classified for which such transforms are effective alternatives to direct graph optimizations. In all these efforts, practical implementations are emphasized in order to advance the boundary of effective computation

Factors of gender pay gap in the highest wages of employees in the Slovak republic

The article contains the results of empirical analysis of data on one percent of employees with the highest salaries in the Slovak Republic in 2020. The starting point for the analysis there is 11,570 anonymized individual values of average gross monthly wage and also personal data of the employees whose wage exceeded the 99th percentile of the sample survey The Informational System on Labour Costs, implemented in the Slovak Republic since 1992 by the company Trexima Bratislava. The aim of the article is to assess the gender pay gap for the best-earning men and women and assess the significance of the impact of selected factors that contribute it. Given the availability of data the monitored factors of the gender pay gap there are education, region of residence, the type of occupation, and the categorized age of employees. To achieve the objective, selected quantitative methods were used, namely methods of descriptive statistics and statistical inference, as goodness-of-fit tests, chi-squared tests of independence and machine learning methods, as normalized Shannon entropy and regression decision tree models. The results of analyses by these methods have been preferably presented in a graphical form. Based on the application of the above methods the significant wage differences by gender at the highest wages (over the 99th percentile of the sample) and significant impact of monitored factors has been confirmed not only on the gender pay gap, but also on the structure of their employment. The results of the analyses lead to the conclusion that the significant wage differences by gender at the highest wages are caused precisely by unequal representation of men and women on the different levels of the monitored factors

A life-span perspective on life satisfaction

The German population is ageing due to decreasing birth rates and increasing life expectancy. To sustain the German pension system, legal retirement age is increased step by step to 67 years. This raises questions about how to enable and motivate older individuals to work that long. Hence, it is important to understand whether they represent a homogeneous group that can be addressed through specific measures and instruments. Life-span theory points to systematic changes as well as increased heterogeneity with age. For example, work motivation does not generally decline with age but becomes increasingly task-specific, depending on changing life goals and individual adaptation processes in adult development. In this empirical study we analyse age heterogeneity with regard to current life satisfaction and life satisfaction domains (measured as satisfaction with work, income, family and health) that represent personal utilities individuals strive for. For our analysis we use data collected as part of a representative German longitudinal data study (SOEP1). We find increasing heterogeneity in current life satisfaction, satisfaction with work, family life, and health with age. Thus, common mean level analyses on age effects yield only limited informative value. The heterogeneity of older adults should be taken into account when motivating and developing older workers

A life-span perspective on life satisfaction

The German population is ageing due to decreasing birth rates and increasing life expectancy. To sustain the German pension system, legal retirement age is increased step by step to 67 years. This raises questions about how to enable and motivate older individuals to work that long. Hence, it is important to understand whether they represent a homogeneous group that can be addressed through specific measures and instruments. Life-span theory points to systematic changes as well as increased heterogeneity with age. For example, work motivation does not generally decline with age but becomes increasingly task-specific, depending on changing life goals and individual adaptation processes in adult development. In this empirical study we analyse age heterogeneity with regard to current life satisfaction and life satisfaction domains (measured as satisfaction with work, income, family and health) that represent personal utilities individuals strive for. For our analysis we use data collected as part of a representative German longitudinal data study (SOEP1). We find increasing heterogeneity in current life satisfaction, satisfaction with work, family life, and health with age. Thus, common mean level analyses on age effects yield only limited informative value. The heterogeneity of older adults should be taken into account when motivating and developing older workers