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With the rapid progress of information technology, it has become the demand of the times to use the power of the Internet to inherit and develop Chinese traditional culture. Given the challenge of users searching for learning content in massive online resources, personalized recommendation systems have become the key to improving user experience. This paper focuses on building a personalized Chinese traditional culture learning platform based on collaborative filtering and popularity recommendation, aiming to stimulate users’ interest in learning traditional culture through intelligent algorithms. In terms of technical implementation, this study relies on the Django framework and MySQL database to build a feature-rich and user-friendly traditional cultural learning platform, supporting users’ diverse learning needs, and providing flexible learning paths and abundant learning resources. Experimental evaluation shows that the hybrid algorithm of collaborative filtering and popularity recommendation proposed in this paper has improved recommendation performance compared to existing methods, effectively enhancing user satisfaction and platform activity. This platform not only promotes the digital dissemination of traditional culture, but also provides users with a highly autonomous and personalized learning experience, meeting the contemporary society’s pursuit of diversified and personalized cultural learning.

This study aims to understand how local enterprises establish brand awareness through tough entrepreneurship as well as focus on international markets so as to inject new ideas and to promote brand value. Examining this issue experimentally on B coffee company with data from Taiwan and China during 2010 to 2012, we use Network Meta-Frontier Data Envelopment Analysis to estimate the cross-strait performances of this firm in order to examine their differences and to propose direction for subsequent improvement. The empirical results are as follows. First, from 2010 to 2012 the channel scale and output in Taiwan is superior to that in China. Second, the performance of China’s channels is on the decline with large fluctuations, whereas Taiwan’s channels are getting better. Third, analyzing the average efficiency value of both sides comprehensively, we find that Taiwan’s channels perform more steadily due to a longer time of establishment and more mature allocation.

This paper introduces Price-to-Earnings Ratio Network (PEN) analysis as an alternative to mean–variance analysis for portfolio optimization. The equivalence among Price-to-Earnings (P/E) ratios, node degree distribution and capital allocation distribution is established with a Havel–Hakimi network structure. Such equivalences allow network entropy to be a measure of portfolio diversification and robustness. Our empirical analysis finds a linear correlation between in-sample network entropy and out-of-sample portfolio returns. Then, a return-entropy efficient frontier is introduced to interpret the return-diversification trade-offs. Further, we compare the out-of-sample performance of portfolios optimized with PEN analysis against those optimized with mean–variance analysis, showing that PEN-optimized portfolios outperform mean–variance efficient portfolios in returns, given a low-to-medium P/E ratio level. This outcome accords with the P/E effect that stocks with low P/E ratios are undervalued and will provide increased returns in the future. In addition, regarding global financial risk events such as the financial crisis in 2008, the Euro debt crisis in 2013 and Brexit in 2016, this study finds that the PEN-optimized portfolio size increased significantly (even to more than 300 stocks) to mitigate systemic risk, while mean–variance efficient portfolios were not sufficiently diversified.

One of the most essential operations in biological sequence analysis is multiple sequence alignment (MSA), where it is used for constructing evolutionary trees for DNA sequences and for analyzing the protein structures to help design new proteins. In this research study, a new method for solving sequence alignment problem is proposed, which is named improved tabu search (ITS). This algorithm is based on the classical tabu search (TS) optimizing technique. ITS is implemented in order to obtain results of multiple sequence alignment. Several variants concerning neighborhood generation and intensification/diversification strategies for our proposed ITS are investigated. Simulation results on a large scale of datasets have shown the efficacy of the developed approach and its capacity to achieve good quality solutions in terms of scores comparing to those given by other existing methods.

This paper introduces a Multi-Agent based Optimization Method for Combinatorial Optimization Problems named MAOM-COP. In this method, a set of agents are cooperatively interacting to select the appropriate operators of metaheuristics using learning techniques. MAOM-COP is a flexible architecture, whose objective is to produce more generally applicable search methodologies. In this paper, the MAOM-COP explores genetic algorithm and local search metaheuristics. Using these metaheuristics, the decision-maker agent, the intensification agents and the diversification agents are seeking to improve the search. The diversification agents can be divided into the perturbation agent and the crossover agents. The decision-maker agent decides dynamically which agent to activate between intensification agents and crossover agents within reinforcement learning. If the intensification agents are activated, they apply local search algorithms. During their searches, they can exchange information, as they can trigger the perturbation agent. If the crossover agents are activated, they perform recombination operations. We applied the MAOM-COP to the following problems: quadratic assignment, graph coloring, winner determination and multidimensional knapsack. MAOMCOP shows competitive performances compared with the approaches of the literature.

The recommender systems community is paying great attention to diversity as key qualities beyond accuracy in real recommendation scenarios. Multifarious diversity-increasing approaches have been developed to enhance recommendation diversity in the related literature while making personalized recommendations to users. In this work, we present Gaussian Cloud Recommendation Algorithm (GCRA), a novel method designed to balance accuracy and diversity personalized top-N recommendation lists in order to capture the user's complete spectrum of tastes. Our proposed algorithm does not require semantic information. Meanwhile we propose a unified framework to extend the traditional CF algorithms via utilizing GCRA for improving the recommendation system performance. Our work builds upon prior research on recommender systems. Though being detrimental to average accuracy, we show that our method can capture the user's complete spectrum of interests. Systematic experiments on three real-world data sets have demonstrated the effectiveness of our proposed approach in learning both accuracy and diversity.

Resource Selection is an important step in a federated search environment. The goal of this work was to improve the collection selection process by selecting collections in terms of relevance and diversity, to best answer a user's query. Sampled documents from the Central Sample Database are first ranked by Indri retrieval algorithm and later re-ranked by a Mean-Standard deviation method that reduces uncertainty and improves diversity of collection sources. A comparative evaluation with the R-based diversification metrics shows that the proposed method significantly outperforms the baseline diversification methods; ReDDE+MMR, ReDDE+MAP-IA and state-of-the-art resource selection methods (ReDDE and CORI) in all metrics.

We present a simple new explanation for the diversification discount in the valuation of firms. We demonstrate that, ceteris paribus, limited liability of equity holders is sufficient to explain a diversification discount. To derive this result, we use a credit risk model based on the value of the firm's assets. We show that a conglomerate can be regarded as an option on a portfolio of assets. By splitting up the conglomerate, the investor receives a portfolio of options on assets. The conglomerate discount arises because the value of a portfolio of options is always equal to or higher than the value of an option on a portfolio. The magnitude of the conglomerate discount depends on the number of business units and their correlation, as well as their volatility, among other factors.

This paper examines the properties that a risk measure should satisfy in order to characterize an investor's preferences. In particular, we propose some intuitive and realistic examples that describe several desirable features of an ideal risk measure. This analysis is the first step in understanding how to classify an investor's risk. Risk is an asymmetric, relative, heteroskedastic, multidimensional concept that has to take into account asymptotic behavior of returns, inter-temporal dependence, risk-time aggregation, and the impact of several economic phenomena that could influence an investor's preferences. In order to consider the financial impact of the several aspects of risk, we propose and analyze the relationship between distributional modeling and risk measures. Similar to the notion of ideal probability metric to a given approximation problem, we are in the search for an ideal risk measure or ideal performance ratio for a portfolio selection problem. We then emphasize the parallels between risk measures and probability metrics, underlying the computational advantage and disadvantage of different approaches.

Given an investment universe, we consider the vector $\rho (w)$ of correlations of all assets to a portfolio with weights $w$. This vector offers a representation equivalent to $w$ and leads to the notion of $\rho$-presentative portfolio, that has a positive correlation, or exposure, to all assets. This class encompasses well-known portfolios, and complements the notion of representative portfolio, that has positive amounts invested in all assets (e.g. the market-cap index). We then introduce the concept of maximally $\rho$-presentative portfolios, that maximize under no particular constraint an aggregate exposure $f(\rho (w))$ to all assets, as measured by some symmetric, increasing and concave real-valued function $f$. A basic characterization is established and it is shown that these portfolios are long-only, diversified and form a finite union of polytopes that satisfies a local regularity condition with respect to changes of the covariance matrix of the assets. Despite its small size, this set encompasses many well-known and possibly constrained long-only portfolios, bringing them together in a common framework. This also allowed us characterizing explicitly the impact of maximum weight constraints on the minimum variance portfolio. Finally, several theoretical and numerical applications illustrate our results.

The main purpose of this paper is to compare the performance of portfolio strategies, comprising investments in emerging markets alone, and those strategies of combining investment in emerging and developed markets. The major finding of this paper is: the portfolio strategy combining investment in emerging markets with that in developed markets does not offer any additional value over a portfolio strategy confining investments to the emerging markets only. This paper, therefore, recommends a strategy that emphasizes country selection from emerging markets alone.

This paper studies optimal growth strategies of a multiproduct firm that invests in the qualities of different products, which have persistent effects on future payoffs and are modeled as a state variable of a stochastic game. We derive a unique Markov perfect equilibrium under a monotonicity condition. At the early stage, the firm focuses on the product with higher quality, and may switch its specialization. If the quality of the specialized good is high enough, the firm diversifies to capture demands for all products. However, the firm may lose its focus on either product and get no demand, due to a moral hazard problem.

This research was directed at the impact of innovation strategy complexity on the breadth of innovation strategy objectives achievement. The context is automotive component manufacturing in developing economies. Given the risk associated with innovation activity, the question is whether firms can improve their success by adopting a complex market access strategy. The methodology was survey based, utilizing data on the innovation activities of some 530 automotive component manufacturing firms obtained by a questionnaire applied in the Pune region in India, Beijing region in China and in South Africa. Path analysis by structural equation modeling was applied to analyze the data for the hypothesized relationships. It was found that strategic complexity in the larger combined country data set is positively and significantly related to a greater breadth of impact of innovation in terms of the market access strategies implemented. It was also observed that diversity of technology and knowledge sources, and innovation types, play a fundamental role in this relationship. The results for individual countries are different from those of all countries combined and yielded varying conclusions. The SA data set was unfortunately too small to meet the requirements for reliable results on its own, but served to support understanding of the influence of its different environment. The findings support the proposition that a diverse innovation strategy should be developed and executed in a business strategy directed at innovation.

Indian outsourcing industry undeterred by recent plummeting growth of global ICT market is experiencing robust growth. Indian ICT firms, by and large, follow success breed success formula by replicating the strategies adopted by the leaders in the market. Two top Indian ICT outsourcing firms have adopted diversification strategy to expand their presence in multiple industry verticals across geographies by offering a diversified range of services. This study, using an entropy analysis, measures the degree of diversification of the top two and most successful Indian outsourcing firm in five different dimensions. The second part of the study analyzes the overall impact of diversification on the performance of the firm. This study reveals that although overall diversification signifies a positive impact on the firm performance, each dimension leaves a different impact on the different firms’ performance.

In many developed economies, the struggle to survive finds many small farms disappearing. Diversification is recognized as an important strategy for sustaining farms of this scale, addressing food security issues and creating a more resilient food system. This study aims to analyze farmers’ intentions to diversify into new business opportunities and how opportunity alertness and risk-taking propensity affect their intentions. These relationships are examined using data collected from 166 small and medium-sized farmers in five regions within Florida. The results indicate that for small and medium-sized farmers, opportunity alertness and risk-taking propensity have a positive effect on diversification intentions across seven different types of activities. Implications are drawn for theory and practice.

Structured Web data are increasingly accessed using information retrieval methods and information retrieval increasingly relies on structured background knowledge. As users' searches are often directed towards finding information about entities rather than text documents, a key affordance of semantic search is the ability to retrieve relevant information about entities more precisely by utilizing the rich structured descriptions and background knowledge. Entity search also poses challenges for information retrieval methods. Entity descriptions are often short and conventional search term matching alone can be insufficient. As a consequence, the search engine should be able to increase the recall of the returned results and select a representative set of entities for a user; to diversify search results. This paper presents an approach to diversify entity search by using semantics present and inferred from the initial entity search results. Our approach utilizes ontologies as a source of background knowledge to improve recall of entity retrieval and independent component analysis to detect independent latent components shared by the entities. The search results are then diversified by selecting a representative set of entities based on their membership in the independent components. We demonstrate the performance of our approach through retrieval experiments conducted by using a real-world dataset composed from four entity databases. The results suggest that our approach can significantly improve effectiveness and diversity of entity search.

The share of developing countries in exports of world services increased from 15% in 2000 to 21% in 2011. Interestingly, in many of the developing economies, the growth in services exports is derived from not just traditional services, but also from modern, high-value, skill-intensive services. Given the rising importance of services, this paper develops a widely applicable methodology for evaluating the contribution of the service sector and the potential of using the sector for growth, employment and trade diversification objectives. We summarize a few key indicators for assessing the performance of the services sector using the available cross-country and bilateral trade data on the services sector. The indicators proposed in this paper are fairly general and draw on cross-country databases; however, to illustrate the methodology we use examples of the following nine countries: Brazil, Chile, Egypt, Hungary, India, Malaysia, Philippines, South Africa, and Ukraine.

This paper studies dynamic asset allocations across stocks, Treasury bonds, and corporate bond indices. We employ a new model where liquidity plays an important role in forecasting excess returns. We document the significant utility benefits an investor gains by optimally including corporate bond indices in his portfolio. The benefits are bigger for lower-grade bonds. We also find that investment-grade indices are different from high-yield indices in that different risks are priced in these two asset classes. One important difference is that there exist positive "flight-to-liquidity" premia in investment-grade bonds, but we find no such premia in high-yield bonds. We calculate the portfolio behavior and the utility benefits for three types of investors, the "sophisticated", the "average" and the "lazy" investor. We provide practical portfolio advice on investing throughout the business cycle and we study how the total allocations and hedging demands vary with the business conditions. In addition, utilizing our model, we evaluate the significance of the liquidity variable information for the investor. We find that the liquidity information greatly enhances the investor's portfolio performance. Finally, further support in the optimality of the strategies is provided by calculating their in- and out-of-sample realized returns for the last decade.

In this study, we investigate how adding Bitcoin can influence the investment portfolios. For this purpose, we consider a portfolio including Bitcoin and the five major sector indices of the Tehran Stock Exchange (TSE). At first, the asset returns are predicted through an estimation model based on higher moments. In the second step, the properties of Bitcoin in the face of other assets in a portfolio are studied by the asymmetric dynamic conditional correlation (ADCC) model. Then, the optimal weights in the portfolios are estimated. Accordingly, we used four portfolio optimization models with different objective functions, including a hybrid function of the higher moments, predicted risk from the ADCC model, and maximizing Sharpe and Sortino ratios. The out-of-sample results showed the relative efficiency of the proposed model in predicting the asset returns in Tehran Stock Exchange. In addition, the results of the ADCC model showed that Bitcoin plays a risk-hedging role for the pharmaceutical and banking sectors in TSE. We also know Bitcoin as a safe haven for the banking, petrochemical, metals, automobile, and pharmaceutical sectors. The results of portfolio selection also prove the effectiveness of adding Bitcoin with a maximum weight of 10% in the investment portfolios.

The European Union (EU) is the third largest energy market on the planet following China and USA, consuming more than 1.6 million tons of oil equivalent, this fact determining the community’s strength. Nevertheless, this strength is valid only in the case when the market is consolidated, therefore, the strategic goal of the community is to avoid splitting the EU’s market into individual member states considering both the economic and political aspects, the force of which is considerably more reduced. This paper aims to comprehensively analyze the energy market of the European Union and determine the key weaknesses which threaten the community’s security in this area. At the same time, it is presupposed to identify the key initiatives through which the European Union aspires to consolidate the energy market’s integrity in the conditions of growing international competition and changing geopolitical environment. To reach these specific goals, a subset of objectives which are expected to be achieved by applying both quantitative and qualitative research methodologies has been selected. The results reached show that the European Union’s efforts are insufficient to consolidate the energy market and deepen the integration, nevertheless, the proposed plans are promising, offering the community a favorable perspective.