Matches in ScholarlyData for { <https://w3id.org/scholarlydata/inproceedings/www2008/paper/367> ?p ?o. }
Showing items 1 to 18 of
18
with 100 items per page.
- 367 creator de-sheng-wang.
- 367 creator hang-li.
- 367 creator tao-qin.
- 367 creator tie-yan-liu.
- 367 creator wen-ying-xiong.
- 367 creator xu-dong-zhang.
- 367 type InProceedings.
- 367 label "Learning to Rank Relational Objects and Its Application to Web Search".
- 367 sameAs 367.
- 367 abstract "Learning to rank is a new statistical learning technology on creating a ranking model for sorting objects. The technology has been successfully applied to web search, and is becoming one of the key machineries for building search engines. Existing approaches to learning to rank, however, did not consider the cases in which there exists relationship between the objects to be ranked, despite of the fact that such situations are very common in practice. For example, in web search, given a query certain relationships usually exist among the the retrieved documents, e.g., URL hierarchy, similarity, etc, and sometimes it is necessary to utilize the information in ranking of the documents. Existing methods, unfortunately, cannot deal with the problem. This paper addresses the issue and formulates it as a novel learning problem, referred to as, 'learning to rank relational objects'. In the new learning task, the ranking model is defined as a function of not only the contents (features) of objects but also the relations between objects. The paper further focuses on one setting of the learning problem in which the way of using relation information is predetermined. It formalizes the learning task as an optimization problem in the setting, and then proposes employing SVM techniques to conduct the optimization. Experimental results show that the proposed method outperforms the baseline methods for two ranking tasks in web search, indicating that the proposed method can indeed make effective use of relation information and content information in ranking.".
- 367 hasAuthorList authorList.
- 367 hasTopic World_Wide_Web.
- 367 isPartOf proceedings.
- 367 keyword "Learning to Rank Relational Objects".
- 367 keyword "Pseudo Relevance".
- 367 keyword "Relational Ranking SVM".
- 367 keyword "Topic Distillation".
- 367 title "Learning to Rank Relational Objects and Its Application to Web Search".