Intelligent recommendation system based on user preferences
A recommendation system and user technology, applied in data processing applications, advertising, business, etc., can solve the problems of not considering each user's personal preferences, lack of sorting and filtering of information, spending a lot of time browsing, etc., to save user selection. time, maintaining accuracy, reducing the effect of picking time
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Embodiment 1
[0076] Such as figure 1 As shown, the present invention includes: a query grouping module, a query processing module, and a user preference acquisition module;
[0077] Query grouping module, used for user query request grouping;
[0078] A query processing module, configured to process user query requests;
[0079] A user preference acquisition module, configured to acquire user preferences and dynamically adjust user preferences;
[0080] Preferably, the user preference acquisition module includes: initialization of preference information and dynamic adjustment of preference;
[0081] Preference information initialization, that is, when the user uses it for the first time, it needs to be parsed according to the query keywords entered by the user as basic preference information; and background preference information is supplemented according to the user's group characteristics.
[0082] The dynamic adjustment of preference information is to score each item of preference in...
Embodiment 2
[0162] In this embodiment, the user needs to actively input initial preference information when using the system for the first time, and the initial preference information selected when registering an account includes "wifi", which means that the user prefers stores with wifi. "Have wifi" is the type 01 preference, and the corresponding weight coefficient is 1 by default. Given that the browsing time threshold is 3 minutes, and the weight increase threshold is 50%, within a period of time of using the system, the statistical user has ordered a total of 9 stores, and the number of times the browsing time exceeds 3 minutes is 12. In the ordering process, 7 out of the 9 shops purchased have wifi, and 9 of the 12 shops visited for more than 3 minutes have wifi. Set ∝=5, use formula (6) The wifi weight adjustment judgment factor is 77.19% greater than the weight increase threshold of 50%, and the independent variable x[i] in the weight coefficient (formula 5) with wifi preferen...
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