Personalized scenic spot recommendation method based on tourist preference modeling
A recommendation method and scenic spot technology, applied in biological neural network models, marketing, market data collection, etc., can solve problems such as difficult for tourists to recommend services, ignore the semantic information of Web services, and fail to consider the different influences of candidate services, etc., to achieve recommendation results Reliable and reasonable, simplifies calculations, increases the effect of accuracy and diversity
Inactive Publication Date: 2019-09-27
GUILIN UNIV OF ELECTRONIC TECH
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This invention does not consider the different impacts of tourists' historical visits to Web services on candidate services, and also ignores the semantic information of Web services themselves, making it difficult to accurately recommend services to tourists
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[0047] The present invention will be described in detail below in conjunction with accompanying drawing and specific embodiment:
[0048] The overall process of the personalized scenic spot recommendation of the present invention is as follows: figure 1 As shown, the model framework is as figure 2 As shown, a kind of personalized attraction recommendation method based on tourist preference modeling of the present invention, the specific steps include:
[0049] Step S1, utilize web crawler technology to gather the scenic spot sequence of tourist's historical tour from major tourism portal websites, for example: (Forbidden City→Badaling Great Wall→Summer Palace→Tiantan Park→Olympic Park), and various tourist attractions scoring information and scenic spots Attribute data, and preprocessing; after cleaning out useless data, number objects such as tourists and scenic spots;
[0050] Step S2, because the rating information collected by tourists in step S1 is an explicit score, a...
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The invention discloses a personalized scenic spot recommendation method based on tourist preference modeling, and the method comprises the steps: collecting data, carrying out the preprocessing, and carrying out the numbering of tourists, scenic spots and other objects; converting the display score into an implicit score, and dividing a positive case scenic spot and a negative case scenic spot; constructing a triple and scenic spot knowledge map, and generating a feature vector and a context feature vector of each scenic spot; generating vector representations of historical tourist tour scenic spots and candidate scenic spots through the KCNN; calculating an influence weight of each historical touring scenic spot of the tourist through the attention network to obtain a preference vector of the tourist to the scenic spot; calculating the scenic spot touring probability of the tourists by using the DNN, and generating scenic spot recommendation lists of the tourists according to the probability from small to large. According to the method, when different influences of historical visiting scenic spots of tourists on the candidate scenic spots are depicted and diversification preferences of the tourists are represented, the attention network is used for calculating the influence weights of the historical visiting scenic spots of the tourists on the candidate scenic spots, so that the recommendation result better conforms to the preferences of the tourists.
Description
technical field [0001] The invention relates to the technical field of scenic spot recommendation, in particular to a personalized scenic spot recommendation method based on tourist preference modeling. Background technique [0002] With the rapid development of the Internet and the explosive growth of online content and information, how to help tourists obtain the information they want from massive amounts of data is crucial for Internet service providers. Therefore, how to construct tourists' preference features based on their historical visit information is the key to a personalized recommendation system. Most of the traditional tourist preference modeling methods are based on the scenic spots visited by tourists in history, and the feature vectors of the scenic spots are generated to describe the preferences of tourists, and then generate personalized recommendations for tourists. However, the scenic spots visited by tourists in history only contain limited feature info...
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IPC IPC(8): G06Q30/06G06Q30/02G06N3/04
CPCG06Q30/0631G06Q30/0201G06N3/045
Inventor 古天龙朱桂明宾辰忠常亮
Owner GUILIN UNIV OF ELECTRONIC TECH
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