Web信息检索结果融合中的按位加权插入合并算法

在Internet中,由于海量数据的多样性,在分布式数据集合上进行有效的检索就成为Web信息检索的一种必要方式。由此,引出多个检索结果的融合问题。对不同检索结果的相似度评分可能完全不可比的情况,本文给出一种新的解决方案:按位加权插入合并算法。在18GB的大规模web标准测试集上的实验证明,该算法始终能够提高综合检索性能,且分布数据集检索结果越好,则合并后性能改善越多。其中系统平均精度提高接近10%,突破了传统方法对分布数据集结果合并的综合效果总是低于使用集中数据集检索的性能局限。

Rank-based Weighted Insertion Results Fusion Algorithm in Web IR

According to the variety of huge amount of web pages in Internet , it has been necessary to today's Web IR to search effectively on distributed collections. Therefore , the retrieval results fusion problem is derived. In this paper , a novel rank-based weighted insertion results fusion algorithm is proposed. Though it is possible that similarity scores of different results are absolutely incomparable , the proposed algorithm works effectively. Experimental results on 18GB large-scale Web standard test collection show the weighted insertion result fusion strategy enhances retrieval performance consistently. When the performances of distributed results are improved , the enhancement increases as well , which reaches to 10%. Furthermore , it also breaks the limitation in traditional result fusion studies that the final result merged by distributed collections is always worse than that of using single central database.