By a News Reporter-Staff News Editor at Information Technology Newsweekly -- Research findings on Information Technology are discussed in a new report. According to news originating from Singapore, Singapore, by VerticalNews correspondents, research stated, "Although Web search techniques have greatly facilitate users' information seeking, there are still quite a lot of search sessions that cannot provide satisfactory results, which are more serious in Web image search scenarios. How to understand user intent from observed data is a fundamental issue and of paramount significance in improving image search performance."
Our news journalists obtained a quote from the research from Singapore National University, "Previous research efforts mostly focus on discovering user intent either from clickthrough behavior in user search logs (e.g., Google), or from social data to facilitate vertical image search in a few limited social media platforms (e.g., Flickr). This article aims to combine the virtues of these two information sources to complement each other, that is, sensing and understanding users' interests from social media platforms and transferring this knowledge to rerank the image search results in general image search engines. Toward this goal, we first propose a novel social-sensed image search framework, where both social media and search engine are jointly considered. To effectively and efficiently leverage these two kinds of platforms, we propose an example-based user interest representation and modeling method, where we construct a hybrid graph from social media and propose a hybrid random-walk algorithm to derive the user-image interest graph. Moreover, we propose a social-sensed image reranking method to integrate the user-image interest graph from social media and search results from general image search engines to rerank the images by fusing their social relevance and visual relevance."
According to the news editors, the research concluded: "We conducted extensive experiments on real-world data from Flickr and Google image search, and the results demonstrated that the proposed methods can significantly improve the social relevance of image search results while maintaining visual relevance well."
For more information on this research see: Social-Sensed Image Search. ACM Transactions on Information Systems, 2014;32(2):74-96. ACM Transactions on Information Systems can be contacted at: Assoc Computing Machinery, 2 Penn Plaza, Ste 701, New York, NY 10121-0701, USA.
The news correspondents report that additional information may be obtained from P. Cui, Singapore National University, Singapore 117548, Singapore. Additional authors for this research include S.W. Liu, W.W. Zhu, H.B. Luan, T.S. Chua and S.Q. Yang.
Keywords for this news article include: Asia, Information Technology
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