In order to get high-quality web pages, search engines often resort retrieval pages by their rank... more In order to get high-quality web pages, search engines often resort retrieval pages by their ranks. The rank is a kind of measurement of importance of pages. Famous ranking algorithms, including PageRank and HITS, make use of hyperlinks to compute the importance. Those algorithms consider all hyperlinks identically in sense of recommendation. However, we find that the World Wide Web is actually organized with the natural multi-level structure. Benefiting from the level properties of pages, we can describe the recommendation of hyperlinks more reasonably and precisely. With this motivation, a new level-based link analysis algorithm is proposed in this paper. In the proposed algorithm, the recommendation weight of each hyperlink is computed with the level properties of its two endings. Experiments on the topic distillation task of TREC2003 web track show that our algorithm can evidently improve searching results as compared to previous link analysis methods.
LETOR is a benchmark collection for the research on learning to rank for information retrieval, r... more LETOR is a benchmark collection for the research on learning to rank for information retrieval, released by Microsoft Research Asia. In this paper, we describe the details of the LETOR collection and show how it can be used in different kinds of researches. Specifically, we describe how the document corpora and query sets in LETOR are selected, how the documents are sampled, how the learning features and meta information are extracted, and how the datasets are partitioned for comprehensive evaluation. We then compare several state-of-the-art learning to rank algorithms on LETOR, report their ranking performances, and make discussions on the results. After that, we discuss possible new research topics that can be supported by LETOR, in addition to algorithm comparison. We hope that this paper can help people to gain deeper understanding of LETOR, and enable more interesting research projects on learning to rank and related topics.
In order to get high-quality web pages, search engines often resort retrieval pages by their rank... more In order to get high-quality web pages, search engines often resort retrieval pages by their ranks. The rank is a kind of measurement of importance of pages. Famous ranking algorithms, including PageRank and HITS, make use of hyperlinks to compute the importance. Those algorithms consider all hyperlinks identically in sense of recommendation. However, we find that the World Wide Web is actually organized with the natural multi-level structure. Benefiting from the level properties of pages, we can describe the recommendation of hyperlinks more reasonably and precisely. With this motivation, a new level-based link analysis algorithm is proposed in this paper. In the proposed algorithm, the recommendation weight of each hyperlink is computed with the level properties of its two endings. Experiments on the topic distillation task of TREC2003 web track show that our algorithm can evidently improve searching results as compared to previous link analysis methods.
LETOR is a benchmark collection for the research on learning to rank for information retrieval, r... more LETOR is a benchmark collection for the research on learning to rank for information retrieval, released by Microsoft Research Asia. In this paper, we describe the details of the LETOR collection and show how it can be used in different kinds of researches. Specifically, we describe how the document corpora and query sets in LETOR are selected, how the documents are sampled, how the learning features and meta information are extracted, and how the datasets are partitioned for comprehensive evaluation. We then compare several state-of-the-art learning to rank algorithms on LETOR, report their ranking performances, and make discussions on the results. After that, we discuss possible new research topics that can be supported by LETOR, in addition to algorithm comparison. We hope that this paper can help people to gain deeper understanding of LETOR, and enable more interesting research projects on learning to rank and related topics.
Uploads
Papers by Tao Qin