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    Techniques for cross-domain recommendation:A survey
    CHEN Lei-hui, KUANG Jun, CHEN Hui, ZENG Wei, ZHENG Jian-bing, GAO Ming
    Journal of East China Normal University(Natural Sc    2017, 2017 (5): 101-116,137.   DOI: 10.3969/j.issn.1000-5641.2017.05.010
    Abstract （583）   HTML （15）    PDF （865KB）（1338）      
    With the rapid development of information technology and Internet, the available information on the Internet has overwhelmed the human processing capabilities in some commercial applications. Personalized recommendation system is a popular technology to deal with the information overload and recommendation algorithms are the core of it. In the past decades, collaborative filtering recommendation algorithm based on single domain has been widely used in many applications. However, the problems of cold start and data sparsity usually result in overfitting and fail to give desirable performance. The cross-domain recommendation techniques have been a hot topic in the field of recommender systems, which aim to utilize knowledge from related domains to perform or improve recommendation in the target domain. This paper carries out a systematic study and analysis of cross-domain recommendation techniques. First, we summarize the related concepts and the technical difficulties of cross-domain recommendation algorithms. Second, we present a general categorization of cross-domain recommendation techniques and sum up their respective advantages and disadvantages. Finally we introduce the method of performance analysis of cross-domain recommendation algorithm in detail.
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    Research of personalized knowledge search for food safety system
    YUAN Pei-sen, REN Wu-bei, REN Shou-gang, ZHU Shu-xin, XU Huan-liang
    Journal of East China Normal University(Natural Sc    2017, 2017 (5): 117-124,137.   DOI: 10.3969/j.issn.1000-5641.2017.05.011
    Abstract （369）   HTML （13）    PDF （904KB）（530）      
    In the era of big data, knowledge discovery from the mass of data is an important research problem, especially for the user's customized knowledge. In this paper, an integrated search system aiming at personalized re-ranking of food safety knowledge system, PROSK for short, is designed and implemented. Firstly, using the existing search engines, the meta-search engine technique is employed for integrating the results of multiple search engines; then according to the results of the users' click through and the ontology of food safety domain, ranking-based learning algorithm is applied to sort search results adaptively according to the preference profiles. The system integrates the agricultural information from multi-engineers and ranks the query results adaptively and intelligently. This study proposes a feasible solution for ranking of information and knowledge of food safety from multi-engineers adaptively.
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    Fraudulent medical behavior detection based on hybrid approach
    PAN Song-song, ZHANG Wei-jia
    Journal of East China Normal University(Natural Sc    2017, 2017 (5): 125-137.   DOI: 10.3969/j.issn.1000-5641.2017.05.012
    Abstract （434）   HTML （60）    PDF （1192KB）（581）      
    With continuous improvement of medical insurance system, coverage of medical insurance continues to expand. The normal operation of medical insurance funds has been closely related with the vital interests of the people. However, frequent occurrence of fraudulent behaviors such as frequent hospitalization, hospitalization decomposition, abnormal fees threaten the normal operation of funds. This paper firstly used random forest method to select different features according to different diseases. Then the paper applied CBLOF-based and improved CBLOF methods to detect abnormal fees. What's more, we utilized rule-based method to identity frequent hospitalization and hospitalization decomposition. Extensive experiments on real medical claim datasets demonstrate the effectiveness and efficiency of our proposal. Finally, this paper proposed a medical insurance fund supervisory system, which can display results of pivot analysis with the help of Echarts.
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    Modeling multi-dimensional user preference based on the latent variable model
    WANG Shan-lei, YUE Kun, WU Hao, TIAN Kai-lin
    Journal of East China Normal University(Natural Sc    2017, 2017 (5): 138-153.   DOI: 10.3969/j.issn.1000-5641.2017.05.013
    Abstract （552）   HTML （16）    PDF （1000KB）（490）      
    Modeling user preference from user behavior data is the basis of personalization service, score prediction, user behavior targeting, etc. In this paper, multi-dimensional preferences from rating data are described by multiple latent variables and the Bayesian network with multiple latent variables is adopted as the preliminary knowledge framework of user preference. Constraint conditions are given according to the inherence of user preference and latent variables, upon which we propose a method for modeling user preference. Parameters are computed by EM algorithm and structure is established by SEM algorithm with respect to the given constraints. In the case of multiple latent variables, a large amount of intermediate data is generated in modeling, which causes the increasing computational complexity. Therefore, we implement the modeling method with Spark computing framework. Experiments results on the Movielens dataset verify that the method proposed in this paper is effective.
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    A hybrid collaborative filtering recommendation model based on complex attribute of goods
    ZHOU Lan-feng, MA Shuang-ke, FU Zheng, ZHANG Qing
    Journal of East China Normal University(Natural Sc    2017, 2017 (5): 154-161,185.   DOI: 10.3969/j.issn.1000-5641.2017.05.014
    Abstract （357）   HTML （79）    PDF （614KB）（582）      
    Collaborative filtering as the most widely used, the most recommendation algorithm, the shortcomings inherent in the data sparse, cold startpoor data quality and others, and few studies based on commodity price to improve the prediction accuracy. At the same time, facing the full e-commerce market network Navy, the ratings and reviews also indirectly led to the predict a decline in accuracy. Therefore, this paper comprehensive consideration of the user subjective ratings and objective product score, and on this basis, combined with situation pre filtering, social network theory and expert opinions put forward a hybrid collaborative filtering recommendation model, to some extent alleviate the above shortcomings. And through experiment with real online car sales data, the model has higher forecast accuracy than the traditional collaborative filtering, and is more suitable for the commodity with complex attributes.
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