Resource recommendation method based on user emotion information, intelligent device and storage medium
A recommendation method and technology for users, applied in digital data information retrieval, special data processing applications, instruments, etc., can solve the problems of not considering users, unable to be intelligent, humanized users, etc.
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example 1
[0078] Scenario example 1: "Zhang San" likes to listen to music, and the music recommendation system will combine "Zhang San"'s listening habits to recommend him a playlist corresponding to the music genre, but the playlist recommended here does not consider "Zhang San"'s emotion state. By adopting the technical solution of the present invention, the emotional state of "Zhang San" can be monitored in real time, such as "sadness", "anger", "calm", "disgust", "fear", "happy", "surprised", "excited" etc., and combine "Zhang San"'s operating habits on mobile devices (smart devices) to analyze his hobbies, such as "reading", "music", "movies", etc., and finally integrate these features in the recommendation system to recommend Resources that soothe corresponding emotions. For example, if "Zhang San" has been in a sad state for a long time, the present invention will detect in real time that he is in a sad state and loves music, and the recommendation system will select cheerful so...
example 2
[0079] Scene example 2: "Zhang San" is a writer who spends a lot of time on creation. Occasionally, when he is tired, he will read some light articles to soothe his mood, and he can't help laughing when he encounters something particularly funny ; If the technical solution of the present invention is applied to the recommendation system, the recommendation system will select those articles that really make him laugh to recommend according to his responses to different articles.
example 3
[0080] Scenario example 3: "Zhang San" often browses short videos on video websites, and the playback of video resources is automatically controlled by the system; when he sees many of his idols, he always smiles or gives praise, but When he arrives at his favorite idol, he will be very excited, even cheering and screaming; a general recommendation system may recommend related resources for him according to his browsing behavior, but ignores his emotional characteristics; adopting the technical solution of the present invention , not only can find the video resources of his favorite idol according to his emotional characteristics, but can even rank these resources according to the granularity of emotional characteristics.
[0081] The present invention collects and analyzes the user's emotional characteristics in real time, analyzes the user's emotional state based on a deep learning model, and achieves the purpose of optimizing the recommendation system, so that the personaliz...
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