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A Study About Search Engine and Its Ranking Algorithms

Author: WangLi
Tutor: ShuaiJianMei
School: University of Science and Technology of China
Course: Pattern Recognition and Intelligent Systems
Keywords: rank models in search engineer learning to rank construct training set image reranking extract features for image measure similarity between images
CLC: TP391.3
Type: Master's thesis
Year: 2010
Downloads: 457
Quote: 9
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Abstract


Recently learning to rank has become a popular method to build a ranking model for Web search. For the same ranking algorithm, the performance of ranking model depends on the training set. A training sample is constructed by labeling the relevance of a document and a given query by human. However, the number of queries in Web search is nearly infinite and the human labeling cost is expensive. Therefore, it is necessary to select subset of queries to construct an efficient training set. In this paper, we develop a greedy algorithm to select queries, by simultaneously taking the query difficulty, density and diversity into consideration. The experimental results on LETOR and a collected Web search dataset show the proposed method can lead to a more efficient training set.Recently image search engines mainly base on associated textual information. Image reranking is an effective approach to refine the initial text-based search result by mining the visual information of the returned images. And the estimation of visual similarity is the fundamental factor in reranking methods. However, the existing similarity measures are independent of the query. In this paper, we propose a query dependent method by incorporating the global visual similarity, local visual similarity and visual word co-occurrence into an iterative propagation framework. Then we embed the query dependent similarity into random walk rereanking method. The experiments on a collected Live Image dataset demonstrate that the proposed query dependent similarity outperforms the global, local similarity and their linear combination.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Retrieval machine
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