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With the popularity of the Internet and mobile terminals, the amount of movie entertainment information on the Internet has increased dramatically, and users' demand for personalized movie services is growing. Personalized recommendation service can be used to mine the hidden correlation between user's historical information, movie project information, user's historical operation log and data, and recommend the obtained video resources that users may be interested in to users, so as to better serve users. Among them, video recommendation is an important field of recommendation system technology research. The existing film and television recommendations are mainly popular recommendations and related recommendations. Popular recommendations are easy to lead to Matthew effect, while related recommendations are in line with users' preferences to a certain extent, but the degree of personalization is low. Different users often see the same recommendation list on the same play page. This article first analyzes user behavior data in the user behavior data modeling phase, and establishes an initial user behavior data model by combining user scoring behavior and labeling behavior. Finally, based on the improved recommendation algorithm proposed in the previous article, a video recommendation system is designed and implemented. First, the system requirements are analyzed, and then relevant designs are carried out according to the requirements. The system is implemented using the SS2H framework, and the main data table display and functional interface display of the system are given.
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