Hybrid collaborative filtering recommendation algorithm based on project attributes

Abstract

The invention discloses a hybrid collaborative filtering recommendation algorithm based on project attributes. The algorithm comprises the following steps: step 1, generating a user-project scoring matrix and a project-attribute matrix according to user scoring information and project information; step 2, respectively calculating project similarity and user scoring similarity according to the project-attribute matrix and the user-project scoring matrix; step 3, correcting the user score similarity by utilizing the project similarity to calculate the user similarity; step 4, calculating a common score reward factor, a project attribute preference factor and a user confidence coefficient factor to correct user similarity and obtain user final similarity; step 5, selecting the nearest neighbor of the target user according to the final similarity, and predicting the score of the target user on each project based on the score information of all the users in the nearest neighbor; And step 6,recommending the N projects with the highest scores to the target user. The method can be applied to sparse data, and recommendation accuracy can be improved.

Description

technical field [0001] The invention belongs to the field of recommendation systems and relates to an information recommendation technology, in particular to a hybrid collaborative filtering recommendation algorithm based on item attributes. Background technique [0002] With the development of Internet information technology, people have gradually entered the era of information overload from the era of information scarcity. In such an era of information overload, a personalized recommendation system can recommend information that users may be interested in by using information filtering technology to help users quickly find the information resources or commodities they need. In recent years, recommender systems have been widely used in various fields such as e-commerce, music, social networking, and medicine, and have become one of the research hotspots in both industry and academia. [0003] Among personalized recommendation algorithms, collaborative filtering recommendat...

AI Technical Summary

Problems solved by technology

[0004] Traditional user similarity measurement methods have the following deficiencies: 1. Usually, only the user’s common rating information (common rating information is the rating information of different users on the same item) is used to calculate user similarity, and the user-item rating matrix data comparison In the case of sparseness, the common scoring information is relatively rare, which will make the traditional user similarity measurement method inapplicable; 2. The user similarity measurement index usually only uses the scoring information, and does not effectively use the item attribute information; 3. User similarity The performance metrics ignore the influencing factors of user similarity, including the following (1) users who have evaluated more of the same items and item attributes are not considered to have higher similarity; (2) different users’ preferences for item attributes are not considered different; (3) user rating information is not necessarily true and credible

[0005] Due to the above shortcomings of the traditional user similarity measurement method, the collaborative filtering algorithm based on the traditional user similarity measurement method cannot be applied to sparse data and the recommendation accuracy is not high.

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