A Dynamic Individual Recommendation Method Based on Reinforcement Learning

As a widely used recommendation method, collaborative filtering can solve the problem of low level of resource utilization which caused by information overload. At present, in order to exhibiting and searching items, we need to use multipole attributes to describe items. Thus request to particularly distinguish every attribute and realize accurate recommendation. While the collaborative filtering method lose sight of the dynamic regulation of items attributes' importance degree, and it cannot interpose the discrimination of attributes. Aiming at this problem, this paper come up with a dynamic individual recommendation method based on reinforcement learning. This method can dig user's attribute tag preference from operant behavior. It can record user's attributes operate path and recall path. Then we build the award-punishment model of attribute tag, and realize the tag weight dynamic regulation. According to the principle that reinforcement learning system always get max award, we make a tag recommend strategy and give user recommendations in accordance with the preferences. The experimental result show that this method can distinguish the validity of user's click, and realize the tag weight dynamic regulation and give user recommendations in accordance with the preferences.