| Summary: | 碩士 === 國立臺北大學 === 資訊管理研究所 === 101 === In recent years, The health and leisure activities have become very popular, the public engaged in leisure activities has increased day by day and the number of new entrepreneurs in health and leisure industry have also increased continuously. Because of the incubator plans, incubator and tutoring service provided by government, these micro entrepreneurs could operate normally the with appropriate support resources. However, the incubator resources allocation method nowadays could not satisfy the needs for incubator entrepreneurs and there is no actively recommended and real-time matching method or tools for incubator entrepreneurs to use for solving real-time needs or providing resources recommendation with its potential preferences.
Therefore, this research consider the preferences of users, the features of implicit evaluation and the real-time information transfer from Internet of Things (IoT) , it uses the methods of Euclidean distance, top-k query and fuzzy membership function to calculate a recommended results which conforms the preferences and behaviors of users. It can also include the real-time information to solve the real-time resources needs for users. This research will use precision, recall and F1 measure to evaluate the performance and observe the trend changed of recommendation in different parameters which like used times, the numbers of non-duplicate resource and the size of similar group. Results of the experiments reveal the proposed recommendation model can recommend precisely and completely when used times reached to a size , it also can get more than 0.6 precision when the similarity group rate less than 2.5%. We also observe that the recommended results were not influenced by used record centralized or decentralized on some specified items.
Through this recommendation model, we can not only allocate resources effectively but prevent resources idleness. The contribution of this study is propose a novel recommendation model, which combine with the user implicit preference and real-time information, this novel model can provide a recommendation system model for future studies as a reference or comparison.
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