An Algorithm for Allocating Sponsored Recommendations and Content: Unifying Programmatic Advertising and Recommender Systems

Retailing and media platforms recommend two types of items to their users: sponsored items that generate ad revenue and nonsponsored ones that do not. The platform selects sponsored items to maximize ad revenue, often through programmatic auctions, and nonsponsored items to maximize user utility with a recommender system (RS). We develop a binary integer programming model to allocate sponsored recommendations considering dual objectives of maximizing ad revenue and user utility. We propose an algorithm to solve it in a computationally efficient way. Our method is a form of postfiltering to a traditional RS, making it widely applicable in two-sided markets. We apply the algorithm to data from an online grocery retailer and show that user utility for the recommended items can be improved while reducing ad revenue by a small amount. This multiobjective approach unifies programmatic advertising and RS and opens a new frontier for advertising and RS research. We provide an extended discussion of future research topics.     

Achieving destination competitiveness: an importance–performance analysis of Serbia

As a relatively new and under-researched tourism destination, Serbia provides an interesting context to assess destination competitiveness in conditions of global environmental changes and the additional challenges of transition from a socialist economy to a market-based economy. This article uses importance–performance analysis (IPA) to assess the importance of different activities to underpin tourism development in Serbia, as well as the industry's perceived performance in respect of these activities. There are a number of areas in which Serbian tourism industry considers itself to be underperforming in the implementation of activities to maintain destination competitiveness. This article analyses these results in detail using IPA as a diagnostic tool. Particular attention is paid to investigating the implications of the findings for both destination managers and private tourism operators in Serbia that can assist them to develop a focused action agenda to achieve and maintain destination competitive advantage. The approach can be used in other destinations to assess tourism ability to meet the challenges of global trends.     

Functional Foods and the Young

Factors such as raised consciousness about human health, development of the food industry, and medicine and branches of natural science studying the relation between nutrition and health have led to the popularization of foods with proven health effects. Such foods are called functional foods. Although there is growing interest in functional foods, there are several groups of consumers that are, more than others, interested in functional foods. To examine buying behavior and attitudes toward functional food of young consumers (18–30 years old) and to determine the most interested consumers, we conducted a survey using a self-administered questionnaire on a sample of 570 Croatian students. The majority of the respondents purchase functional foods, usually in supermarkets. Apart from taste, the most important functional food attribute is price/quality ratio. Respondents are buying functional foods because they believe that those foods are healthier and safer than other products. Examined market segments were based on respondents’ attitudes toward functional foods. The results of this research could be used in planning further development of the functional foods market for young consumers.     

Markov decision processes under model uncertainty

We introduce a general framework for Markov decision problems under model uncertainty in a discrete-time infinite horizon setting. By providing a dynamic programming principle, we obtain a local-to-global paradigm, namely solving a local, that is, a one time-step robust optimization problem leads to an optimizer of the global (i.e., infinite time-steps) robust stochastic optimal control problem, as well as to a corresponding worst-case measure. Moreover, we apply this framework to portfolio optimization involving data of the . We present two different types of ambiguity sets; one is fully data-driven given by a Wasserstein-ball around the empirical measure, the second one is described by a parametric set of multivariate normal distributions, where the corresponding uncertainty sets of the parameters are estimated from the data. It turns out that in scenarios where the market is volatile or bearish, the optimal portfolio strategies from the corresponding robust optimization problem outperforms the ones without model uncertainty, showcasing the importance of taking model uncertainty into account.