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A social search evaluation method based on friend clustering in lbsn

An evaluation method and friend technology, applied in the field of social search, can solve the problems of affecting search accuracy, single field, lack of generalization of search object fields, etc., and achieve the effect of accurate and objective search results, dense data, and elimination of singular points

Active Publication Date: 2020-11-27
SOUTHEAST UNIV
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  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Some studies pay more attention to the improvement of search speed, and improve the search speed by improving the index structure, ignoring the sparseness of the data set, which affects the search accuracy; some studies analyze specific events that occur at the location point, such as earthquakes, fires, etc., Realize the search for events that occur at specific locations, but lack the generalization of the search object field, the field is relatively single, and cannot better meet the actual needs; some studies are due to the subjective evaluation methods such as similarity metrics and the overload of social search systems Large, resulting in room for improvement in search performance

Method used

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  • A social search evaluation method based on friend clustering in lbsn
  • A social search evaluation method based on friend clustering in lbsn
  • A social search evaluation method based on friend clustering in lbsn

Examples

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Embodiment 1

[0046] Example 1: see figure 1 , figure 2 , a social search evaluation method based on friend clustering in LBSN, described evaluation method comprises the following steps, 1) there are contact information and location information in the Foursquare real data set of crawling, by statistics and analysis to data, extract Contact features, check-in features, evaluation features and time features, a total of 15 data types, including user ID, friend ID, check-in ID, check-in location description, check-in occurrence time zone, check-in location ID, check-in location latitude and longitude, check-in location name, check-in location The type ID of the location, the type name of the check-in location, the time when the check-in occurred, the ID of the evaluation text, the content of the evaluation text, and the time of occurrence of the evaluation, construct a social search model and give a formal description, and filter the data set that occurred in New York. This method is also the ...

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Abstract

The invention discloses a friend clustering-based social search evaluation method for an LBSN. Multi-dimensional characteristics such as location-based information, contact person information and the like are extracted from a Foursquare real data set; a friend clustering-based KNN search algorithm is proposed; a reverse index-based search engine is designed; and in combination with factors such as a distance and the like, a search result is more accurate and the search speed is increased. For enabling the search result to be more accurate, firstly, friends are clustered on the basis of researching user friends. The LBSN belongs to a heterogeneous network, and the data set is relatively sparse, so that data can be denser by clustering; singular points are eliminated, so that adverse influence caused by data sparsity is reduced; secondly, in design of the search algorithm, on the basis of considering conventional social contact influence, two indexes including professional relevance and distance are added, namely, a comprehensive search score, a social contact score and a distance score are considered; and finally, the three indexes are integrated, a linear planning model is built and trained, and the search result is obtained, so that a user is satisfied with the search result.

Description

technical field [0001] The invention relates to an evaluation method, in particular to a social search evaluation method based on friend clustering in LBSN, and belongs to the technical field of social search. Background technique [0002] The development of Online Social Networks (OSNs) has brought great convenience to people's daily life. Today, billions of users are active on OSNs every day, generating a large amount of social information. Gradually, people prefer to search for information through OSNs instead of traditional search engines, and social search emerges as the times require. Due to the shortcomings of traditional search methods such as low precision rate, long user screening time, and consistent search results, in the context of user personalized search, social search relies on traditional search principles and combines user social information to generate personalized search results. search results to improve search accuracy. In particular, the emergence of...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06Q10/06G06Q50/00G06F16/35
CPCG06F16/35G06Q10/0639G06Q50/01
Inventor 曹玖新孙洋周丹丹
Owner SOUTHEAST UNIV
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