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To control the "uniqueness" risk in Public-Private Partnership (PPP) projects of transportation infrastructure, we design a simplified "uniqueness" contract model by incorporating the impact of the initial investment which is based on the Bertrand model. The nonlinear programming method is adopted to derive the optimal "uniqueness" contracts for incumbent private capital, the public, and the social welfare, respectively. The simulation results show that the achievement of the optimal "uniqueness" contract is essentially the result of a compromise between the private capital, the public, and social welfare. The extent to which such a contract reduces the probability of "uniqueness" risk mainly depends on the equilibrium relation between the interests of private capital and the public. The initial investment is not related to the government default when the contract does not take into account the interests of the private capital. Furthermore, the "uniqueness" contracts between private capital and the government are mainly for anticompetitive purpose in the PPP market of transportation infrastructure. Unless the contract terms focus on the improvement of social welfare, entering a "uniqueness" contract will cause social welfare losses. [ABSTRACT FROM AUTHOR]

Course recommendation aims at finding proper and attractive courses from massive candidates for students based on their needs, and it plays a significant role in the curricula-variable system. However, nearly all students nowadays need help selecting appropriate courses from abundant ones. The emergence and application of personalized course recommendations can release students from that cognitive overload problem. However, it still needs to mature and improve its scalability, sparsity, and cold start problems resulting in poor quality recommendations. Therefore, this paper proposes a novel personalized course recommendation system based on deep factorization machine (DeepFM), namely Deep PersOnalized couRse RecommendatIon System (DORIS), which selects the most appropriate courses for students according to their basic information, interests and the details of all courses. The experimental results illustrate that our proposed method outperforms other approaches. [ABSTRACT FROM AUTHOR]