A collaborative filtering recommendation method based on user attribute coupling similarity and interest semantic similarity

Abstract

A collaborative filtering recommendation method for user attribute coupling similarity and interest semantic similarity, including the following steps: Step 1. Collect a large amount of user and item data; Step 2. Preprocess the item content text information, and then perform deep learning word vector model Training to obtain the word vector model; Step 3. Convert the words in the project text into vectors through the word vector model; Step 4. Analyze the user's interest content vector; Step 5. Preprocess the user registration information and extract user attributes; Step 6. Combine user attribute coupling similarity and user interest content semantic similarity to dynamically calculate user's comprehensive similarity; Step 7. Predict score, select the top n items with the highest score as the recommendation set. The invention can improve the quality and reliability of the nearest neighbor user set, and effectively solve the cold start problem and the sparsity problem.

Description

technical field [0001] The invention relates to the field of collaborative filtering recommendation, in particular to a collaborative filtering recommendation method with user attribute coupling similarity and interest semantic similarity. Background technique [0002] With the rapid development of technologies such as cloud computing, big data, and the Internet of Things, various services and user data on the Internet have exploded. These big data contain rich value and great potential, which has brought transformative development to human society. How to quickly and effectively obtain valuable information from complex data to make personalized recommendations for users is the research of recommendation system. key problem. Personalized recommendation system has become a hot spot in academia and industry and has produced many related research results. The recommendation system is based on the user's bias, interests, etc., through the recommendation algorithm to mine the i...

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Problems solved by technology

Document 3 (Chen Liang, School of Information Engineering, Shenzhen University, Shenzhen, Guangdong, Chen Liang, et al. Mobile video recommendation strategy based on DNN algorithm [J]. Journal of Computer Science, 2016, 39(8): 1626-1638.) proposed a A content recommendation method based on a deep learning model, constructing a user interest vector through a word vector model, and filtering and making recommendations on a content-based recommendation method, this method mines the deep semantic relationship between users and items, but ignores the The influence of similar user groups, the recommendation results are specialized, and the lack of diversity

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