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从搜索会话中挖掘潜在的查询功能Mining Broad Latent Query Aspects from Search Sessions |
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| 课程网址: | http://videolectures.net/kdd09_punera_mblqass/ |
| 主讲教师: | Kunal Punera |
| 开课单位: | 雅虎公司 |
| 开课时间: | 信息不详。欢迎您在右侧留言补充。 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Search queries are typically very short, which means they are often underspecified or have senses that the user did not think of. A broad latent query aspect is a set of keywords that succinctly represents one particular sense, or one particular information need, that can aid users in reformulating such queries. We extract such broad latent aspects from query reformulations found in historical search session logs. We propose a framework under which the problem of extracting such broad latent aspects reduces to that of optimizing a formal objective function under constraints on the total number of aspects the system can store, and the number of aspects that can be shown in response to any given query. We present algorithms to find a good set of aspects, and also to pick the best $k$ aspects matching any query. Empirical results on real-world search engine logs show significant gains over a strong baseline that uses single-keyword reformulations: a gain of $14\%$ and $23\%$ in terms of human-judged accuracy and click-through data respectively, and around $20\%$ in terms of consistency among aspects predicted for "similar" queries. This demonstrates both the importance of broad query aspects, and the efficacy of our algorithms for extracting them. |
| 关 键 词: | 目标函数; 搜索引擎; 算法有效性 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-01-13：chenxin |
| 阅读次数: | 32 |