Enriching user search experience by mining social streams with heuristic stones and associative ripples

Authors:
Xiaokang Zhou;Neil Y. Yen;Qun Jin;Timothy K. Shih
Affiliations:
Graduate School of Human Sciences, Waseda University, Tokorozawa, Japan;Graduate School of Human Sciences, Waseda University, Tokorozawa, Japan;Graduate School of Human Sciences, Waseda University, Tokorozawa, Japan;Department of Computer Science & Information Engineering, National Central University, Taoyuan, Taiwan
Venue:
Multimedia Tools and Applications
Year:
2013

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Abstract

Recently, social networking sites such as Facebook and Twitter are becoming increasingly popular. The high accessibility of these sites has allowed the so-called social streams being spread across the Internet more quickly and widely, as more and more of the populations are being engaged into this vortex of the social networking revolution. Information seeking never means simply typing a few keywords into a search engine in this stream world. In this study, we try to find a way to utilize these diversified social streams to assist the search process without relying solely on the inputted keywords. We propose a method to analyze and extract meaningful information in accordance with users' current needs and interests from social streams using two developed algorithms, and go further to integrate these organized stream data which are described as associative ripples into the search system, in order to improve the relevance of the results obtained from the search engine and feedback users with a new perspective of the sought issues to guide the further information seeking process, which can benefit both search experience enrichment and search process facilitation.