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162results about How to "Improve preference" patented technology

Program recommendation system, program view terminal, program view program, program view method, program recommendation server, program recommendation program, and program recommendation method

An object of the invention is to eliminate a program having a high general preference, and recommend a program customized to a preference of a specific user. A reserved information receiver (230) in a program recommendation server (3) receives reserved information transmitted by a program viewing terminal. A reserved information DB (235) stores the reserved information with respect to each of the users. A community reserved information statistics section (236) defines the users as a first group, based on the received reserved information, and the reserved information stored with respect to each of the users. A total user reserved information statistics section (237) defines the users as a second group including the first group. A recommended program creator (239) generates recommended information customized to the first group, based on an aggregate calculation result of the reserved information of the users belonging to the first group, and an aggregate calculation result of the reserved information of the users belonging to the second group. A recommendation transmitter (240) transmits the recommended information to the program viewing terminal. The program viewing terminal reproduces a program based on the recommended information.
Owner:VERIVA SYST SDN BHD

Personalized scenic spot recommendation method based on tourist preference modeling

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.
Owner:GUILIN UNIV OF ELECTRONIC TECH
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