Research On Personalized Ranking Method Based On Socialized Annotation Information

With the development of Internet technology and the advent of the information age,massive data is presented to people.How to quickly and accurately obtain the information users need from such vast and growing data has always been a key issue in the field of information retrieval.In the process of information retrieval,the query words usually have short,summary and ambiguous characteristics,which cannot accurately express the user's query intent,resulting in inaccurate search results.In addition,due to the differences in the background and needs of users,the traditional search model can't meet the different needs of different users under the same conditions because of its versatility,and it is difficult to obtain search results that vary from person to person.In recent years,due to the emergence of social labeling system and the idea of individualization,many scholars have begun to explore the application of social labeling to personalized information retrieval.Its effectiveness has been well verified,but there is still a certain Room for improvement.This article focuses on how to more effectively use social annotation information to improve the effectiveness of personalized information retrieval.The social annatation usually represents the user's insights into the labeling resources,but in reality,these tabbed information can also be regarded as a powerful extension of webpage content.Using them to extend the personalized score of the document is an existing personalization.A commonly used means of sorting methods.However,in reality,on the one hand,due to some privacy protection mechanisms,the web pages marked by users and the labels used by them are extremely limited,and the problem of sparse data often appears in the social annotation system,which undoubtedly brings personalized information retrieval.On the other hand,users have different interests and different degrees of preference for different web pages.In the existing personalized sorting method,the relationship between users and web content is not the same,resulting in a calculated document personalized sorting score is not accurate.Based on this,based on the previous research,this paper mainly has the following two contributions:(1).We proposes a personalized sorting method combining word vector technology and user similar network.The method first constructs a similar network of users by using the co-occurrence relationship of user annotation behaviors,so that the judgment of similar users is more accurate.Then,in order to reduce the impact of the sparseness of the socialized annotation information,the user's annotation information is expanded by the idea of collaborative filtering,so that the user can more express the interest information for the document.Secondly,considering the possible semantic connection between tag words,word vector technology is used to transform tag words into higher-dimensional expressions,so as to improve the inaccurate matching of query words and document attributes when data sparseness.Finally,it is verified by experiments that this method can improve the accuracy of personalized information retrieval in the case of not increasing the time and space overhead,and can improve the user's retrieval experience.(2).We also proposes a personalized approach that integrates user interest preferences.This method is aimed at the problem that the relationship between the user and the overall content of the document is not fully considered in the current personalized information retrieval.On the basis of the previous work,it attempts to use the existing socialized annotation information to fully exploit the users in the social annotation system.The relationship between the tag,the document and the document introduces the user's preference information at the document level.Firstly,the documents are clustered,and then the user's interest model is constructed to calculate the user's preference for different types of documents.Finally,the user's preference for different types of documents is used to personalize the documents.Experiments on real experimental data sets show that the proposed method can improve the accuracy of personalized search results and improve user satisfaction.