Information recommending method based on social network

Abstract

The invention discloses an information recommending method based on a social network. The information recommending method includes the following steps that first, trust degree and similarity between users are calculated, and a user relation matrix is constructed through weighted values; second, the users are clustered through a community discovering algorithm, and then a closest neighbor set of the users is formed; third, scores are predicted, and a recommending list is generated. The information recommending method based on the social network can achieve the following advantages that first, the cold start problem is solved: trust degree is introduced into the method, if enough neighbors cannot be obtained according to the common grading articles in the recommending process, trustable friends can serve as the start point of prediction, and thus the cold start problem can be relieved, and user coverage can be improved; real time performance is improved: community division is performed on the user network through the community discovering algorithm commonly used in social network analysis, in other words, same user interests are clustered, and thus the time for finding the neighbor set of the users is greatly shortened, and the real time performance of the information recommending response is improved.

Description

technical field [0001] The present invention relates to the technical field of network information, in particular to an information recommendation method based on a social network. Background technique [0002] The rapid development of the Internet and the ever-increasing information resources have led to a sharp increase in the information index. The information service field is facing the problem of "rich information resources, but difficult to obtain useful information", which brings a great information burden to people. On the one hand, there is an "information overload" phenomenon (information overload) caused by a large number of data resources on the network; on the other hand, users cannot obtain the information resources they need. Recommendation systems (recommendation systems), as an important method of serving in the "information push" mode, are the main means to solve the problem of information overload. For information that is difficult to obtain, recommend mo...

AI Technical Summary

Problems solved by technology

However, there are still some limitations: the content that can be analyzed is limited, it is only information that can be represented by a series of feature sets, and it cannot effectively process multimedia information such as sound, pictures, and video; users can receive and Liked to recommend similar items in the past, but was unable to discover new and interesting products for users, and the recommended content was single; unable to deal with quality, style, or opinion

However, the disadvantages of collaborative filtering algorithm are also very obvious, namely "cold start" problem, data sparse problem, scalability problem, etc.

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