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논문 기본 정보

자료유형
학술저널
저자정보
Sangdon Joo (Gachon University) Sukyung Seo (Gachon University) Youngmi Yoon (Gachon University)
저널정보
한국컴퓨터정보학회 한국컴퓨터정보학회논문지 한국컴퓨터정보학회 논문지 제22권 제12호(통권 제165호)
발행연도
2017.12
수록면
117 - 123 (7page)

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초록· 키워드

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The personalized search algorithm is a search system that analyzes the user`s IP, cookies, log data, and search history to recommend the desired information. As a result, users are isolated in the information frame recommended by the algorithm. This is called `Filter bubble` phenomenon. Most of the personalized data can be deleted or changed by the user, but data stored in the service provider‘s server is difficult to access. This study suggests a way to neutralize personalization by keeping on sending random query words. This is to confuse the data accumulated in the server while performing search activities with words that are not related to the user. We have analyzed the rank change of the URL while conducting the search activity with 500 random query words once using the personalized account as the experimental group. To prove the effect, we set up a new account and set it as a control. We then searched the same set of queries with these two accounts, stored the URL data, and scored the rank variation. The URLs ranked on the upper page are weighted more than the lower-ranked URLs. At the beginning of the experiment, the difference between the scores of the two accounts was insignificant. As experiments continue, the number of random query words accumulated in the server increases and results show meaningful difference.

목차

Abstract
I. Introduction
II. Preliminaries
III. Method
IV. Experiment Result and Analysis
Ⅴ. Conclusions
REFERENCES

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UCI(KEPA) : I410-ECN-0101-2018-004-001664490