Research And Design Of Hybrid Similarity Recommendation Mechanism Based On Collaborative Filtering

The rapid development of the popularity of the Internet and information technology, the network data is exponential growth, especially in recent years, social networking and e-commerce sites springing up and rapid development, the phenomenon of "information overload" and "data explosion". These phenomenon have become an important problem that can not be ignored by businesses and users, and the emergence of personalized recommendation service is an effective means to solve this situation. Collaborative filtering algorithm is one of the most widely used techniques in the research of recommendation algorithm, such as user-based and item-based collaborative filtering mechanism and algorithm.However, most of the current recommendation systems have the problem of cold start and data sparsity. In addition, only using the traditional similarity to calculate the nearest neighbor of the project, will ignore the user’s behavior, failed to pay attention to the user’s interest in a variety of hobbies. At the same time, the calculation of the user to find the nearest neighbors to ignore the trust between users, but also will have an impact on the quality of recommendation. Therefore, the optimization of the similarity calculation, a more reasonable solution to data sparsity and cold start problems and improve the recommendation accuracy and provide users with a more personalized recommendation service has become a hot research direction in the field of filtering recommendation collaborative.According to the above problems existing in traditional collaborative filtering, this paper proposes a hybrid similarity recommendation algorithm based on collaborative filtering. The algorithm consider user behavior, using mixed models, improved similarity metric is calculated, the project attribute correlation and the modified cosine similarity to the linear combination, proposed a hybrid similarity calculation method to calculate the nearest neighbor set of projects. At the same time, research and analysis of traditional collaborative recommendation system without the introduction of trust relationship between users, through user trust relationship matrix to calculate the degree of trust between users, combined with the hybrid similarity to calculate user nearest neighbors, will eventually be a score of user similarity and user trust degree combination, forming new similarity measure method of project target prediction score, and ultimately the formation of Top-N recommendation list object to recommend to users.Paper through comparison of different algorithms, in Epinions dataset the general research algorithm and hybrid similarity of user multi interests recommendation algorithm, user-based collaborative filtering recommendation algorithm, user trust based on the collaborative filtering recommendation algorithm, the trust of users based on clustering recommendation algorithm of the four algorithms are compared. The experimental results show that the proposed hybrid algorithm based on collaborative filtering is better than the other four algorithms in the mean absolute error value, which proves the feasibility and effectiveness of the proposed algorithm. The recommendation algorithm not only improves the accuracy of the recommendation, but also to a certain extent, it can solve the problem of cold start of users, so that users have a more humane recommended experience, help to promote the development of the mechanism of recommendation.