• Journal of Digital Contents Society
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• v.11 no.1
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• pp.105-115
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• 2010

Search engines provide web documents that are related to user's query. However, using only the query terms that user provided, it is hard for search engines to know user's exact intention and provide the very matching web documents. To remedy this problem, search systems are needed to exploit personalized search technologies. In this paper, we propose not only a novel personalized query recommendation scheme based on folksonomy but also a new personalized search service architecture which reduces the risk of privacy violation while enabling search service providers to provide other various personalized services such as personalized advertisement.

• International Journal of Computer Science & Network Security
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• v.24 no.9
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• pp.150-156
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• 2024

With the advent of personalized search engines, a myriad of approaches came into practice. With social media emergence the personalization was extended to different level. The main reason for this preference of personalized engine over traditional search was need of accurate and precise results. Due to paucity of time and patience users didn't want to surf several pages to find the result that suits them most. Personalized search engines could solve this problem effectively by understanding user through profiles and histories and thus diminishing uncertainty and ambiguity. But since several layers of personalization were added to basic search, the response time and resource requirement (for profile storage) increased manifold. So it's time to focus on optimizing the layered architectures of personalization. The paper presents a layout of the multi agent based personalized search engine that works on histories and profiles. Further to store the huge amount of data, distributed database is used at its core, so high availability, scaling, and geographic distribution are built in and easy to use. Initially results are retrieved using traditional search engine, after applying layer of personalization the results are provided to user. MongoDB is used to store profiles in flexible form thus improving the performance of the engine. Further Weighted Sum model is used to rank the pages in personalization layer.

• Journal of the Korean Society for Library and Information Science
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• v.45 no.4
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• pp.77-102
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• 2011

The purpose of this study is to design, implement and evaluate a personalized search system using gathered information on users to provide more accurate search results. For this purpose, a hybrid-based user profile is constructed by using subject classification. In order to evaluate the performance of the proposed system, experts directly measured and evaluated MRR, MAP and usability by using the Korean journal articles of science and technology DB. Its performance was better than the general search system in the area of "Computer Science" and "Library and Information Science". Especially better results were shown when tested on ambiguous keywords. Evaluation through in-depth interviews proved that the proposed personalized search system was more efficient in looking up and obtaining information. In addition, the proposed personalized search system provided a variety of recommendation systems which proved helpful in navigating for new information. High user satisfaction ratings on the proposed personalized search system were another proof of its usefulness. In this study, we were able to prove through expert evaluation that the proposed personalized search system was more efficient in information retrieval.

• Journal of Internet Computing and Services
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• v.12 no.2
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• pp.63-73
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• 2011

In general, conventional search engines provide the same search results for the same queries of users, and however such techniques do not consider users' characteristics. To overcome this problem, we need a new way of personalized search which returns customized search results according to users' preference. In this paper, we propose a concept network profile-based personalized web search system in which the concept network is developed for accumulating users' characteristics. The concept network-based user profile is used to expand initial search queries to achieve personalized search. The concept network is a network structure of concepts where each concept is generated whenever each query is submitted, and it can be defined as a set of keywords extracted from the selected documents. Furthermore, we have improved the concept networks by augmenting intent keywords of each concept with a set of classification tags, called folksonomy, assigned to each document. For an additional personalized search technique, we propose a new re-ranking method that analayzes the degree of overlapped search results.

• Journal of KIISE:Databases
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• v.36 no.4
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• pp.321-326
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• 2009

Since the existing Web search engines don't sufficiently reflect user's search intent, it is very difficult to find out accurate information that users want to find. Therefore, a lot of researches, study for personalized search, to enhance satisfaction of Web search results by analyzing search pattern and applying it to search are in progress in these days. Web searchers can more efficiently find information and easily obtain appropriate information through the personalized search. In this paper, we propose the personalized search based on community through the analysis of web users' query patterns and interest. Consequently, when applying query frequency, interest and community to web search, we are able to the confirm that the search results which hit to the search intent of the individual are provided.

• The KIPS Transactions:PartB
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• v.13B no.5 s.108
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• pp.533-540
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• 2006

The semantic web environment promise semantic search of heterogeneous data from distributed web page. Semantic search would resuit in an overwhelming number of results for users is increased, therefore elevating the need for appropriate personalized ranking schemes. Culture Finder helps semantic web agents obtain personalized culture information. It extracts meta data for each web page(culture news, culture performance, culture exhibition), perform semantic search and compute result ranking point to base user profile. In order to work efficient, Culture Finder uses five major technique: Machine learning technique for generating user profile from user search behavior and meta data repository, an efficient semantic search system for semantic web agent, query analysis for representing query and query result, personalized ranking method to provide suitable search result to user, upper ontology for generating meta data. In this paper, we also present the structure used in the Culture Finder to support personalized search service.

• Journal of Digital Contents Society
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• v.11 no.3
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• pp.357-365
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• 2010

Personalized web search engine provides personalized results to users by query expansion, re-ranking or other methods representing user's intention. The personalized result page includes URL, page title and small text fragment of each web document. which is known as snippet. The snippet is the summary of the document which includes the keywords issued by either user or search engine itself. Users can verify the relevancy of the whole document using only the snippet, easily. The document summary (snippet) is an important information which makes users determine whether or not to click the link to the whole document. Hence, if a search engine generates personalized document summaries, it can provide a more satisfactory search results to users. In this paper, we propose a personalized document summary system for personalized web search engines. The proposed system provides increased degree of satisfaction to users with marginal overhead.

• Journal of the Korean Society for information Management
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• v.23 no.4 s.62
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• pp.179-196
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• 2006

This study proposes an expanded model of personalized search service based on community activities on a Korean Web portal. The model is composed of defining subject categories of users, providing personalized search results, and recommending additional subject categories and queries. Several experiments were performed to verify the feasibility and effectiveness of the proposed model. It was found that users' activities on community services provide valuable data for identifying their Interests, and the personalized search service increases users' satisfaction.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.3
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• pp.587-594
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• 2019

This paper proposes a personalized web search technique that produces ranked results reflecting user's query intents and individual interests. The performance of personalized search relies on an effective users' profiling strategy to accurately capture their interests and preferences. User profile is a data set of words and customized weights based on recent user queries and the topic words of web documents from their click history. Personal profile is used to expand a user query to the personalized query before the web search. To determine the exact meaning of ambiguous queries and topic words, this strategy uses WordNet to calculate semantic similarities to words in the user personal profile. Experimental results with query expansion and re-ranking modules installed on general search systems shows enhanced performance with this personalized search technique in terms of precision and recall.

• The Journal of the Korea Contents Association
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• v.10 no.11
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• pp.461-467
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• 2010

In this paper, the framework of a personalized search system designing and can perform features which are suitable for each contents by using various contents a feature was developed. In addition, by using various contents including the domestic paper, patent, trend information, and etc., it applied to the various algorithm of the personalized search system.