Interest activity recommendation method based on geographic grid
A recommendation method and grid technology, applied in electrical digital data processing, special data processing applications, instruments, etc., can solve the problem of low accuracy of recommendation results, and achieve the goal of meeting individual needs, reducing complexity, and improving accuracy. Effect
Active Publication Date: 2022-02-15
CHINESE ACAD OF SURVEYING & MAPPING
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At present, due to the influence of data sparsity and cold start, the recommendation results obtained by the recommendation algorithm for points of interest may be less accurate
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[0066] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, but not to limit the present invention. In addition, it should be noted that, for the convenience of description, only some structures related to the present invention are shown in the drawings but not all structures.
[0067] The invention lies in: using the user's check-in history records in the location social network to model the user's check-in activities, and propose a method for recommending personalized urban interest activities. Based on the geographic grid, the method integrates the user's geographical preference and time preference for the urban area, analyzes the spatiotemporal behavior of the user's check-in in the city, and recommends more suitable interest activities to the target user.
[0068] see figure 1 shows...
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An interest activity recommendation method based on a geographic grid comprises the following steps: dividing a research area into regular grids, and establishing a personal interest grid area for each user through sign-in frequency and preference deviation ratio parameters so as to deduce space activity preferences of the users; capturing other similar user activity preferences by adopting a non-negative tensor decomposition method, and cooperatively establishing time activity preferences of the users; and fusing the space activity preference and the time activity preference by adopting a context-aware fusion method to jointly determine the interest activity recommended to the user. According to the model, the sparsity of user sign-in data is improved based on a geographic grid and a tensor decomposition method, quantitative analysis is carried out on user interest activities, the interest activity recommendation accuracy is improved, and the recommendation result meets the personalized requirements of users.
Description
technical field [0001] The present application belongs to the technical field of location recommendation, and in particular relates to a geographic grid-based interest activity recommendation method. Background technique [0002] With the rapid development of location-based social network (Location-based Social Network, LBSN) and mobile devices, mining potential user preferences, activity trajectories and life patterns from the accumulated massive user data and check-in data has become the core of location-based services. a central link. Location recommendation has become an important technical means in this link. At present, due to the influence of data sparsity and cold start, the recommendation results obtained by the recommendation algorithm for points of interest may be less accurate. At the same time, in many cases, people usually don't need very precise location. As a result, the research on interest activity recommendation algorithms and applications emerged as th...
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IPC IPC(8): G06F16/9535G06F16/9537
CPCG06F16/9535G06F16/9537
Inventor 仇阿根赵习枝张志然陶坤旺张福浩陈颂陈才
Owner CHINESE ACAD OF SURVEYING & MAPPING