Collaborative filtering recommendation system and method for assisting in preference acquisition by utilizing electroencephalogram signal

Abstract

The invention discloses a collaborative filtering recommendation system and method for assisting in preference acquisition by utilizing an electroencephalogram signal. Through electroencephalogram signal data, a user emotion tendency is calculated and an implicit feedback acts on preference scoring of commodities browsed by a user; and in a recommendation process, a classification result of the electroencephalogram signal data is brought into a calculation category, so that the recommendation accuracy of the recommendation system is improved. By adopting the recommendation method for deeply mining user preferences, the user can quickly find required data from overloaded information data. On the other hand, along with miniaturization and productization of electroencephalogram equipment, therecommendation method provided by the invention provides a new direction for next development of the recommendation system in combination with professional knowledge of the field of brain-computer interfaces.

Description

technical field [0001] The invention belongs to the technical field of information recommendation and pattern recognition, and in particular relates to a collaborative filtering recommendation system and method for assisting preference acquisition by using electroencephalogram signals. Background technique [0002] Emotions often involve people's immediate needs and subjective attitudes, often have complex interrelationships with other psychological processes, and are a comprehensive state of thinking, feeling, and behavior. Since ancient times, China has said the seven emotions of "joy, anger, sorrow, joy, sorrow, and fear". Lange et al. proposed an emotion classification model based on two-dimensional space based on pleasure and arousal, so that people's different emotional experiences can be mapped to a two-dimensional emotional space through these two dimensions. [0003] The brain is the most complex part of human organs, it contains a wealth of physiological and psych...

AI Technical Summary

Problems solved by technology

[0006] The existing collaborative filtering system only uses the known preference data of the current user or other users for some items to predict the potential preference of the current user for other items, and does not take into account the emotional changes of the user when browsing products and the similarity relationship between items. , the user's demand for recommended content often changes in different emotional states

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