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  • 1.
    Boman, Magnus
    et al.
    Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
    Sandin, Anna
    Implementing an agent trade server2006In: Decision Support Systems, ISSN 0167-9236, E-ISSN 1873-5797, Vol. 42, no 1, p. 318-327Article in journal (Refereed)
    Abstract [en]

    An experimental server for stock trading autonomous agents is presented and made available, together with an agent shell for swift development. The server, written in Java, was implemented as proof-of-concept for an agent trade server for a real financial exchange.

  • 2. Ding, Shuai
    et al.
    Wang, Zeyuan
    Wu, Desheng
    Stockholm University, Faculty of Social Sciences, Stockholm Business School. Chinese Academy of Sciences, China.
    Olson, David L.
    Utilizing customer satisfaction in ranking prediction for personalized cloud service selection2017In: Decision Support Systems, ISSN 0167-9236, E-ISSN 1873-5797, Vol. 93, p. 1-10Article in journal (Refereed)
    Abstract [en]

    With the rapid development of cloud computing, cloud service has become an indispensable component of modern information systems where quality of service (QoS) has a direct impact on the system's performance and stability. While scholars have concentrated their efforts on the monitoring and evaluation of QoS in cloud computing, other service selection characteristics have been neglected, such as the scarcity of evaluation data and various customer needs. In this paper, we present a ranking-oriented prediction method that will assist in the process of discovering the cloud service candidates that have the highest customer satisfaction. This approach encompasses two basic functions: ranking similarity estimation and cloud service ranldng prediction that takes into account customer's preference and expectation. The comparative experimental results show that the proposed method outperforms other competing methods.

  • 3. Pan, Yuchen
    et al.
    Wu, Desheng
    Stockholm University, Faculty of Social Sciences, Stockholm Business School. University of Chinese Academy of Sciences, China.
    Olson, David L.
    Online to offline (O2O) service recommendation method based on multi -dimensional similarity measurement2017In: Decision Support Systems, ISSN 0167-9236, E-ISSN 1873-5797, Vol. 103, p. 1-8Article in journal (Refereed)
    Abstract [en]

    With the rapid development of information technology, consumers are able to search for and buy services or products online, and then consume them in an offline store. This emerging ecommerce model is called online to offline (O2O) service, which has attracted business and academic attention. The large number of O2O services on the Internet creates a scalability problem, creating massive but highly sparse matrices relating customers to items purchased. In this paper, we proposed a novel O2O service recommendation method based on multidimensional similarity measurements. This approach encompasses three similarity measures: collaborative similarity, preference similarity and trajectory similarity. Experimental results show that a combination of multiple similarity measures performs better than any one single similarity measure. We also find that trajectory similarity performs better than the rating-based similarity metrics (collaborative similarity and preference similarity) in sparse matrices.

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