Name
Abstract | ||
|---|---|---|
Traditional query suggestion methods mainly leverage click-through information to find related queries as recommendations, without considering the semantic relateness between queries. In addition, few studies use click-through distribution in diversifying query suggestions. To address these issues, we propose a novel and effective framework to generate relevant and diversified query suggestions. We combine query semantics and click-through information together to generate query suggestion candidates which are highly relevant to original query, we use click-through distribution to diversify the candidates. We evaluate our method on a large-scale search log dataset of a commercial engine, experimental results indicate that our framework has significantly improved the relevance and diversity of suggested queries by comparing to four baseline methods. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-25255-1_48 | APWeb |
Keywords | Field | DocType |
query suggestion,diversity,click-through information,query semantics | Data mining,Leverage (finance),Information retrieval,Computer science,Semantics | Conference |
Volume | ISSN | Citations |
9313 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 13 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zheng Hai-Tao | 1 | 142 | 24.39 |
| Zhang Yi-Chi | 2 | 0 | 0.34 |