【摘要】 【目的】解决传统的兴趣点推荐基于简单上下文,推荐同一类别中最流行的、最便宜或者最接近的对象,导致推荐准确度低的问题。【方法】将兴趣点推荐作为一个排序问题,设计基于内嵌空间排序支持向量机模型(Embedded Space Ranking SVM,ESSVM),根据不同特征进行兴趣点分类,利用签到数据捕获用户的喜好,并使用机器学习模型调整不同属性在排序中的重要性。【结果】本方法与基于用户的协同过滤(User CF)、基于兴趣点的协同过滤(VenueCF)、兴趣点流行度(PoV)和最近邻推荐(NNR)等推荐策略相比,不仅可捕获个别异构偏好,而且可减少训练推荐模型的时间消耗。【局限】收集和整合多个基于位置的社交网络上下文信息工作量大;减少本方法的时间和类别的粒度时,还可能面临数据稀疏性问题。【结论】本方法考虑了时间变化对用户偏好的影响,以及用户在不同时段访问的位置类别,通过将有用的上下文信息与签到记录相结合,提供个性化的建议。更多还原

【Abstract】 [Objective] The traditional interest point recommendation methods are mostly based on simple context and can only recommend objects that are the most popular, cheapest or the closest to interest points. Combines time, category information with user’s check-in records, and make up for the shortcomings of traditional interest points recommendation methods with characteristics of user’s preference, and provide support for improving recommendation accuracy. [Methods] The interest point recommendation is considered as a sorting problem. In this paper, ESSVM(Embedded space ranking SVM) is proposed based on embedded spatial sorting support vector machine model to classify interest points according to different features. User preferences are captured using check-in data, and machine learning models are used to adjust the importance of different attributes in sorting. [Results] Compared with User CF, Venue CF, Po V, NNR and other recommendation methods, ESSVM not only can capture individual heterogeneous preferences, but also can reduce the consumption of the training model of time. [Limitations] Collecting and integrating different contextual information from different location based social networks(LBSNs) will take a lot of work. In addition, if users reduce the granularity of time and class in ESSVM, they maybe need to solve the problem of data sparseness. [Conclusions] This method takes account of the impact of time variation on user preferences, as well as the location categories that users visit at different times. By providing useful contextual information and check-in records, it provides personalized suggestions.更多还原