Name
Abstract | ||
|---|---|---|
In contrast to traditional Web search, where topical relevance is often the main selection criterion, news search is characterized by the increased importance of freshness. However, the estimation of relevance and freshness, and especially the relative importance of these two aspects, are highly specific to the query and the time when the query was issued. In this work, we propose a unified framework for modeling the topical relevance and freshness, as well as their relative importance, based on click logs. We use click statistics and content analysis techniques to define a set of temporal features, which predict the right mix of freshness and relevance for a given query. Experimental results on both historical click data and editorial judgments demonstrate the effectiveness of the proposed approach. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1145/2187836.2187915 | WWW |
Keywords | Field | DocType |
click log,traditional web search,news search,historical click data,editorial judgment,topical relevance,joint relevance,relative importance,content analysis technique,increased importance,content analysis,learning to rank | Learning to rank,Data mining,Content analysis,World Wide Web,Information retrieval,Computer science,Selection criterion | Conference |
Citations | PageRank | References |
6 | 0.47 | 22 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hongning Wang | 1 | 925 | 54.89 |
| Anlei Dong | 2 | 582 | 30.85 |
| Lihong Li | 3 | 2390 | 128.53 |
| Yi Chang | 4 | 1463 | 86.17 |
| Evgeniy Gabrilovich | 5 | 4573 | 224.48 |