A personalized search engine model based on RSS user's interest

Jiang, Zongli; Deng, Xiaojia

doi:10.1109/icfcc.2010.5497371

Cited by 3 publications

(2 citation statements)

References 3 publications

(2 reference statements)

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“…Search results produced by this network will be more responsive to users' search needs. To set up the interest model of users' browsing history, Jiang and Deng [23] built the model of RSS (Real Simple Syndication) based on user implicit feedback. Based on individual users' automated search feedback, the RSS interest model continues to update and improve personal search accuracy.…”

Section: A Personal Searchmentioning

confidence: 99%

A Study of Optimizing Search Engine Results Through User Interaction

Chen

2020

IEEE Access

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In this paper, we propose a method for optimizing search engine results based on user interaction. This method generates different search results mainly through the user's operation on different search topics. There are two main differences between our method and the traditional personalized search method: personal privacy and storage space. First, traditional personal search methods need to record search and click records for individual users. However, these records have great personal privacy issues for users; especially in recent years, there have been personal privacy leaks that occurred in many large online companies (such as Facebook and Yahoo). Secondly, because different users need to record their own search records, the size of the storage space is closely related to the number of users and the amount of search records stored. However, the sum of individual users' search and click records is a huge storage space in today's Internet environment. To avoid these two issues for traditional personalized search methods, this study proposes a storage-free approach based on individual users operating on different search topics. In general, in addition to avoiding personal privacy and storage space issues, our method can also achieve optimal linear time in generating personalized search results. INDEX TERMS Document clustering, machine learning, natural language processing, personalized topic search, user log.

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Section: A Personal Searchmentioning

confidence: 99%

A Study of Optimizing Search Engine Results Through User Interaction

Chen

2020

IEEE Access

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“…Beyond a simple query matching, FeedMil also gives a new search experience that focused on quality and topic relevance. A personalized search engine model based on RSS's user interest was proposed [8]. The user model adopts three layers, the top layer is the user model node which represents identification number.…”

Section: Related Workmentioning

confidence: 99%

Blogs Search Engine Using RSS Syndication and Fuzzy Parameters

Mohammed

Husni

2012

Proceedings of the International Conference on Advances in Image Processing and Compuation Techniques

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The rapid development of the internet eventually increases the number of internet users triggering the need for an intelligent search engine that is able to minimize the search on world wide web (WWW) and find relevant information as requested. To overcome the issue of finding relevant information as well as minimizing the search on WWW, this paper proposes a search engine that is specifically designed and built using RSS syndication and fuzzy Parameters to search for information contained in blogs. The blogs search engine consists of three main phases: 1) crawling using RSS feeds algorithm; 2) indexing weblogs algorithm; and 3) searching technique using fuzzy logic. In RSS crawling process, the RSS feeds need to be gathered to extract useful information such as title, links, time published, and description. Next, indexing weblogs uses the links to retrieve the blog sites for text processing and for constructing the indexing database. In order to retrieve such information requested or queried by any user, an interface is provided to enable the blog search based on keyword with associated degree of importance. The density of keyword is then computed from the indexing database. The rank of the pages is computed by using fuzzy weighted average. The experiment resulted in mean average precision of 81.7% of total system performance.

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Interactive Topic Search System Based on Topic Cluster Technology

Chen

2020

Inf Syst Front

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A personalized search engine model based on RSS user's interest

Cited by 3 publications

References 3 publications

A Study of Optimizing Search Engine Results Through User Interaction

A Study of Optimizing Search Engine Results Through User Interaction

Blogs Search Engine Using RSS Syndication and Fuzzy Parameters

Interactive Topic Search System Based on Topic Cluster Technology

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