Crossover services involve deep convergence of services in different domains. Requirements analysis of crossover services often requires the collaboration of engineers in different domains and various organizations. The industry has a demand for sophisticated requirements engineering (RE) methods to address challenges brought by crossover and convergence. However, many methods proposed by researchers have not been widely used in the industry, and the reason is that these methods provide technological solutions to the problems and challenges that may not be considered accurate or even real by practitioners in the crossover services industry. A great effort is needed to explore what needs, expectations, and constraints that RE approaches must satisfy and promote the adoption of novel RE approaches in the crossover services industry. The authors have conducted an industrial study to gain an in-depth understanding of practitioners' needs concerning RE research and method development. The study involved qualitative interviews as well as questionnaires to collect data. The objective of this study is to investigate issues, challenges, and practices of crossover services RE from practitioners' perspectives and to reveal potential guidelines for researchers and tools developers. Findings from five aspects of RE methods are reported and some research directions for future crossover services RE research are provided.Despite the advantages of crossover services, enterprises still face huge challenges when migrating to crossover services. Crossover services involve the coordination of multipleThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Product ratings are popular tools to support buying decisions of consumers, which are also valuable for online retailers. In online marketplaces, vendors can use rating systems to build trust and reputation. To build trust, it is really important to evaluate the aggregate score for an item or a service. An accurate aggregation of ratings can embody the true quality of offerings, which is not only beneficial for providers in adjusting operation and sales tactics, but also helpful for consumers in discovery and purchase decisions. In this paper, we propose a hierarchical aggregation model for reputation feedback, where the state-of-the-art feature-based matrix factorization models are used. We first present our motivation. Then, we propose feature-based matrix factorization models. Finally, we address how to utilize the above modes to formulate the hierarchical aggregation model. Through a set of experiments, we can get that the aggregate score calculated by our model is greater than the corresponding value obtained by the state-of-the-art IRURe; i.e., the outputs of our models can better match the true rank orders.

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