Research on Intelligent Recommendation System for Film and Television Content Based on Computer Vision

Traditional recommendation systems mainly rely on users' historical behavior data, which is difficult to capture the deep-seated characteristics of film and television content and its potential semantic associations. In order to solve this problem, this paper proposes a comprehensive intelligent recommendation framework. This study explores an innovative recommendation system that provides highly personalized film and television content recommendations by integrating collaborative filtering algorithms, knowledge graphs, and attention mechanisms. Firstly, the user-project association matrix is constructed through the collaborative filtering algorithm to capture the similarity between users and the correlation between projects. The approach not only takes into account explicit feedback from users (e.g., ratings), but also incorporates implicit feedback (e.g., watch time, pause frequency) to gain a more complete understanding of user preferences. Next, the knowledge graph technology is used to construct a comprehensive graph structure containing the interaction information of movies, actors, directors and users, so as to capture the complex associations between nodes. In order to enhance the real-time and dynamic adaptability of recommendations, a self-attention mechanism is introduced, which enables the model to automatically adjust the recommendation weight according to the user's immediate interests. In addition, for the problem of cold start, the prior knowledge in the knowledge graph is used for pretraining, which effectively alleviates the problem of recommending new users and new film and television content. Experimental results show that the proposed intelligent AI recommendation system significantly improves the recommendation effect and is better than the existing methods in multiple evaluation indicators. Specifically, the accuracy of recommendations has increased by 15%, and user satisfaction has also improved significantly. The system is not only suitable for online film and television platforms, but also provides new ideas and technical support for personalized recommendations in other fields.