Finding Dimensions for Queries∗ Zhicheng Dou1, Sha Hu2, Yulong Luo3, Ruihua Song1, and Ji-Rong Wen1 1Microsoft Research Asia; 2Renmin University of China; 3Shanghai Jiaotong University 1{zhichdou, rsong, jrwen}@microsoft.com;
[email protected];
[email protected] ABSTRACT dimensions cover the knowledge about watches in five unique We address the problem of finding multiple groups of words aspects, including brands, gender categories, supporting fea- or phrases that explain the underlying query facets, which tures, styles, and colors. The query “visit Beijing” has a di- we refer to as query dimensions. We assume that the impor- mension about popular resorts in Beijing (tiananmen square, tant aspects of a query are usually presented and repeated in forbidden city, summer palace, ...) and a dimension on sev- the query’s top retrieved documents in the style of lists, and eral travel related topics (attractions, shopping, dining, ...). query dimensions can be mined out by aggregating these sig- Query dimensions provide interesting and useful knowl- nificant lists. Experimental results show that a large number edge about a query and thus can be used to improve search of lists do exist in the top results, and query dimensions gen- experiences in many ways. First, we can display query di- erated by grouping these lists are useful for users to learn mensions together with the original search results in an ap- interesting knowledge about the queries. propriate way. Thus, users can understand some important facets of a query without browsing tens of pages. For ex- ample, a user could learn different brands and categories of Categories and Subject Descriptors watches.