Design and Implementation of Game Recommender System
Abstract
The emergence and popularization of the Internet has brought a lot of
information to users and met the needs of users for information in the information
age. However, with the rapid development of the network, the corresponding
amount of information on the Internet has increased significantly, which makes
users unable to screen and distinguish the availability of information in the face of
a large amount of information, and ultimately leads to a decline in the utilization
efficiency of information Situation. In order to solve this problem, recommendation
system came into being. According to the information needs and interests of users,
it can recommend the information and products that users are interested in to users
through personalized calculation. Game recommendation system is one of them.
This paper studies and implements a game recommender system design
scheme combining web technology and similarity calculation in deep learning:
Based on the multi-directional analysis and design of ranking model, this scheme
can ensure the beauty and simplicity of the system interface, realize the
diversification of recommender forms and the simplification of user use, and realize
game recommendation, game scoring and game search at the same time It can
update and modify the recommended content in real time according to the user's
individual and overall information acquisition and feedback.
Key Words: deep learning, web design, similarity algorithm, score
recommendation, related recommendation
Commented [A1]: 11-1-Abstract 标题 1-段前(要求：17.00
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