Collaborative filtering recommendation algorithm fusing social trust influence

Abstract

The invention provides a collaborative filtering recommendation algorithm fusing social trust influence, which comprises the following steps: step 1, preprocessing score matrix data of a project by users, filling and deleting unnecessary data, and calculating similarity among the users according to scores of the users; step 2, calculating a neighbor user set of the users; and 3, calculating the correct recommendation number of the user j to the user i, the local trust degree between the users and the global trust degree of each user according to the scoring matrix data of the user to the project. According to the invention, sparse user scoring information is fully mined; an implicit trust network is constructed, a local trust degree and a global trust degree are calculated through the scoring data; according to the method, the local trust degree and the global trust degree are combined to obtain the implicit trust degree, trust propagation of the users is considered for trust data between the users, the display trust relationship of the users is effectively expanded, and the problems of score matrix data sparseness and user cold start are relieved.

Description

technical field [0001] The invention relates to the technical field of computer technology recommendation methods, in particular to a collaborative filtering recommendation algorithm that integrates social trust influence. Background technique [0002] With the rapid increase of network information, using recommendation algorithm to deal with information overload has become an effective method. In the development of social media and business systems, while bringing convenience to users, the information of users and projects also shows explosive growth. Facing the problem of user and item information overload, using recommender systems has become an effective tool for filtering information. According to additional information such as user preferences, item characteristics, user-item interaction history, time and space, a corresponding recommendation list can be generated for a specific user. One of the most important challenges in recommender systems is the cold-start probl...

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Problems solved by technology

[0004] The present invention provides a collaborative filtering recommendation algorithm that integrates the influence o...

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