A personalized recommendation method integrating social information

Abstract

The invention relates to the technical field of personalized recommendation, in particular to a personalized recommendation method fusing social information. In accordance with the user-item scoring matrix, calculating scoring similarity between users, and screening the nearest neighbor set of scoring; then, according to the nearest neighbor set, calculating the prediction score of the target user; calculating the social similarity between users according to the social network information of users, and screening the social nearest neighbor set; according to the social nearest neighbor set, calculating the prediction score of the target user; by combining the two, predicting the target user 's rating value of the project, and descending the order, recommending the K items with the highest rating to the target user, and generating the recommendation list. Finally, the experimental results show that the performance of the proposed personalized recommendation method is better than the current recommendation method, which can effectively improve the accuracy of recommendation, so as to alleviate the data sparseness and cold start problems.

Description

technical field [0001] The present invention relates to the technical field of personalized recommendation, in particular to a personalized recommendation method integrating social information. Background technique [0002] With the application of social network media such as Weibo and the explosive growth of content information generated by users, facing the increasingly serious problem of information overload, it becomes more difficult to find valuable information in massive data. The development of recommendation technology can mine the user's preference tendency based on the user's historical rating information, so as to make personalized recommendations for users, but the traditional personalized recommendation methods have problems of data sparsity and cold start, and the accuracy of the recommendation results low and high error rate. [0003] Traditional social network recommendation systems mostly use content-based recommendation methods, but do not make full use of...

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Problems solved by technology

[0002] With the application of social network media such as Weibo and the explosive growth of content information generated by users, facing the increasingly serious problem of information overload, it becomes more difficult to find valuable information in massive data. The development of recommendation technology can mine the user's preference tendency based on the user's historical rating information, so as to make personalized recommendations for users, but the traditional personalized recommendation methods have problems of data sparsity and cold start, and the accuracy of the recommendation results low, high error rate

[0003] Traditional social network recommendation systems mostly use content-based recommendation methods, but do not make full use of the social network information formed between users. Therefore, how to effectively use the social network relationship between users to dig out accurate user preferences and improve recommendation Accuracy, reducing the recommendation error rate, and improving user experience are one of the hotspots that urgently need research.

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