Item information recommending method based on user network data

Abstract

The invention relates to an item information recommending method based on user network data. The method is characterized by including the following steps of firstly, obtaining item demand information of a user through the user network data, and setting up a user demand feature library; secondly, setting up an item information feature library according to item network data; thirdly, matching the user demand features with the item information features through a text and semantic similarity algorithm, calculating the similarity between item information and user demands, conducting sequencing and filtering, and finally generating the recommending result; fourthly, feeding the generated recommending result back to the user demand feature library and the item information feature library, and conducting training and renewing. Compared with the prior art, the method has the advantages of being comprehensive in information, wide in application range and the like.

Description

technical field [0001] The present invention relates to the field of network technology, in particular to a method for recommending item information based on user network data Background technique [0002] With the development of information technology and the Internet, people have gradually entered the era of information overload from the era of information scarcity, and the simultaneous presentation of massive information is a great challenge for both information consumers and information producers: for information Consumers, on the one hand, it is difficult for users to find the parts they are interested in, and on the other hand, it also makes a lot of information that few people care about become "dark information" in the network, which cannot be obtained by ordinary users; It is also very difficult for the information produced by oneself to stand out and attract the attention of the majority of users. Traditional search algorithms can only present the same sorting resu...

AI Technical Summary

Problems solved by technology

[0008] However, both content-based recommendation algorithms and collaborative filtering recommendation algorithms have their own shortcomings. Content-based recommendation algorithms need to extract the characteristics of items. Automatic feature extraction methods are widely used in text data, but it is difficult to apply to multimedia data: and Items recommended for a user are limited to items similar to the user's history, resulting in poor diversity

For new users without historical records, it is difficult to recommend through the content-based recommendation algorithm, and there is a cold start problem for new users. The collaborative filtering recommendation algorithm can overcome the low degree of automation and the lack of rich recommendation results due to the consideration of the similarity between users. Disadvantages, however, the collaborative filtering recommendation algorithm is based on a large number of historical data sets, so there are sparse problems and cold start problems. It is difficult for new users without historical records and new items that have not had positive feedback from users to pass the collaborative filtering recommendation algorithm In addition, how to dynamically extract user needs and preference information from user social network data, combine recommendation algorithms with user social network data to generate recommendation results, and solve the problem of information overload for users. Applications

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